FPL Poker Tables GW4

Good news everyone. I’ve hit upon a genius idea to make my spreadsheets more accurate. Simply add 1 to every team’s predicted score, to take into account the effect of the new handball rule!

I’ve previously cited grounds for treating my spreadsheet’s predictions with caution at the start of the season (the impact of new transfers; the suspect form of several teams post-lockdown; and, the unknown quantity that are newly promoted teams), but it became clear last weekend that I’d missed the most distorting effect of all. The new handball rule is hands down (or up, away from the body, and outside the body line) the single biggest factor making results and scorelines so unpredictable this season.

After three gameweeks, my spreadsheets are yet a register a single correct score forecast! Last season, they managed an average of 2 per week, which may not sound like many, but is actually decent. That said, they had the BHA 1 MUN 2 scoreline right, up until the 4th minute of additional time, and the CRY 1 EVE 1 prediction would have been two out of two if you chalk off the ridiculous penalty awarded to the visitors because Joel Ward had forgot to superglue his arms to his sides!

Who saw MCI 2 LEI 5 and WHU 4 WOL 0 coming? Not my spreadsheet, that’s for sure! FUL 0 AVL 3 and BUR 0 SOU 1 were the other results to diverge from my model’s predictions markedly.

All I have to do to cheer myself up whenever I am feeling disappointed by my spreadsheet’s limitations, however, is compare its predictions with those deemed most probable by bookmakers. Half of the predictions tallied, but the bookies were out by more in 4 of the other 5, and this was enough to make the mean absolute error (MAE) and mean square error (MSE) of their predictions considerably worse than my spreadsheet (see below).

correlation test of predicted and actual goals

By way of comparison, my spreadsheets averaged a MAE of 0.8 last season, which is actually very respectable. Note, the lower the MAE the better. For ease of understanding, a MAE of 1.0 would equate to being one goal out (on average) for each team’s score prediction.

In other good news, the expectation is that my spreadsheet will become progressively more accurate with each passing gameweek, so let us turn now to the predictions for GW4. Here are the scorelines suggested by putting together each team’s most likely number of goals (see below). The probability of each scoreline is shown alongside.

17.8% for WOL 2 FUL 0 is the highest probability my spreadsheet has given to any result this season so far. That said, WOL 1 FUL 0 has an even higher probability of 20.2%! Remember, the method used to arrive at these correct score forecasts doesn’t necessarily equate to the scorelines with the highest probability. In fact, only 4 of these do so. The highest probability for the other scorelines are as follows: EVE 1 BHA 1; LEE 0 MCI 2; SOU 1 WBA 1; ARS 1 SHU 1; and, AVL 0 LIV 1.

So, you’re probably wondering why I don’t just list the scorelines with the highest probability? The short answer is because the method I prefer yields better results. Last week, for example, going with the highest probability scorelines would have fared worse than the bookies, with a MAE of 1.42.

The long answer would invite you to take the example of ARS vs SHU this weekend to understand why I prefer the method I use. Whilst it is true that 1 – 1 has a higher probability than 2 – 1, it is also true (paradoxically) that ARS have a higher probability of scoring twice than they do just the once, and I deem that to be the deciding factor.

Further good news for GW4 is that there are 8 teams deemed more likely to score twice than once and, in descending order of expected goals, they are shown in the table below:

good news for most of the big hitters

As highlighted by my Season Review blog earlier this month, Palace are nowhere near as defensively resolute away from home, and Werner sellers may come to regret their impatience this weekend. Aubameyang sellers last week may likewise be nervously eyeing his forthcoming fixture vs SHU, who will be without O’Connell. Meanwhile, Jimenez buyers disappointed by his blank vs WHU last weekend can be hopeful of belated returns vs FUL, seemingly the league’s whipping boys. There is plenty of encouragement here too for owners of the usual suspects from SOU, EVE, MCI, MUN and LIV.

In further welcome news for Jimenez owners, WOL also feature in the top 5 projected highest scoring teams in 2 of the following 3 gameweeks (see below).

WOL sit in the top 5 for expected goals in 3 of the next 4 gameweeks

Courtesy of my spreadsheet’s player points prediction model, Rashford is my sole MUN attacker, and I was pleasantly surprised just now to discover that he’s the least owned of the Reds’ front four. Especially so, given that he’s at the summit of my spreadsheet’s GW4 expected FPL points table (see below). Some of you will remember that Fernandes was ranked highest of the quartet last week, and justifiably so as it happened.

Intriguingly, 2 defenders from CHE feature in the top 4 here, which is territory normally reserved for the marauding fullbacks of LIV.

4 defenders made the cut

Scepticism was expressed last week about Sterling not featuring in the GW3 table, and I anticipate similar protests this week about the notable absences here of Fernandes, DCL, Jimenez, Aubameyang and KDB, so for context they are provided in the best of the rest table shown below.

a pack of Wolves

Remember though, these are averages. In reality, players points tend to polarise between high and low. So, in the absence of clean sheet points, a midfielder scoring a goal and earning a bonus point one week (8), but blanking the next (2), would have averaged 5 points.

At the other end of the pitch, meanwhile, there are much stronger candidates for clean sheets this week than there were last, although the unusually high probabilities assigned to WOL and CHE (see below) fly in the face of the fact that they conceded 7 goals between them in GW3! The hope is that the 4 goals shipped vs WHU last week was just a bad day at the office for the former, whilst the chalk and cheese performances of the latter’s defence playing at home rather than away was something else picked up on by my Season Review blog.

Historically, my spreadsheet’s clean sheet probability calculations have been its strongest suit, so it was disappointing that none of the 4 teams highlighted in green last week (MUN, LIV, MCI and CRY) managed to keep a clean sheet.

I kept a record last season, up until lockdown, of how my model’s clean sheet probabilities fared against those implied by bookies odds, and made popular by @FPL_Salah, and on only 2 out of 12 occasions did the bookies do better than my spreadsheet (1 tie). Last week, the bookies were on average 0.52% more accurate though, so that makes it 3 out of 13 for them now. Time hasn’t permitted me to look back at how they fared over the first 2 gameweeks, so I will report back on those after the International Break.

Just when confidence in the WOL backline has taken a knock, they actually top my clean sheet probability projections for the next 6 gameweeks, so it will be interesting to see how they fare this weekend before possibly committing to a double up in GW6.

Interestingly, one of the promoted teams are currently riding high in this same table in 4th place, which has me sitting up and taking notice, and bodes well for me making savings in my team’s defence.

Another thing I tested last season was the accuracy of my spreadsheet’s longer-term forecasts, and the results were very encouraging indeed. I compared the projections for the following 5 gameweeks with current gameweek only predictions provided by “the world’s most powerful predictive fantasy football algorithm”. And, despite that model having the informational advantage of being up-to-date prior to each of the following 5 gameweeks, my spreadsheet outperformed it in each category I ran correlation tests on (number of goals teams scored, correct score forecasts, and mean absolute error). In other words, even from 5 gameweeks away, my spreadsheet’s projections proved more accurate than up to the minute predictions provided elsewhere.

I raise this here because it’s high time I let it be more widely known that my 6GW spreadsheets have been on sale for some time now, since Project ReStart in fact. They are at a cheap as chips cost of £2 each, or the heavily discounted price of £30 for a season ticket.

I have hitherto been unsure about broadcasting this development, and consequently only sold sheets to those who took the initiative to enquire about buying them. In light of the increasing number of take-ups I’ve had lately, however, and the positive feedback received, there seems no good reason for me to continue keeping to the shadows, so please feel free to DM me any questions you may have about purchasing your own copies, or click here to go directly to my PayPal link.

May the GW4 flop be with you!

Coley a.k.a. FPL P0ker PlAyer @barCOLEYna

P.s. Since my last blog I’ve had the honour of being signed up to the Scout Network. Scout is the only fantasy football service I’ve ever paid for over the past seven seasons, and I’ve always believed their membership subscriptions offer great value for money. Especially as their Members Area played a key role in me winning my main money mini-league three years out of four! I tend to assume that everyone who takes FPL seriously is already a member these days, but if you’re not and you’re considering joining, then you can find out more here.

FPL Poker Tables GW3

Honestly, as the goals flew in at the weekend at an unprecedented rate, I felt like declaring the weekly exercise of formulating predictions suspended until further notice. Until GW17 perhaps when I’d have sufficient new season data to trust in them more. The ridiculously high conversion rates achieved by goalscorers this season so far is making a mockery of xG based models, and I was all for binning mine off in a fit of pique!

Then today, I reminded myself of 2 things: a) regression to the mean is unlikely to be a redundant concept; and, b) my spreadsheet’s score predictions weren’t as bad as those given the highest probability of occurring by the bookmakers, who remain the undisputed benchmark for such things. My sheet may have underestimated the volume of goals that would be scored, but by nowhere near as much as the bookies did (see below).

Those who read my blog last week will recall my making a strong case for why my spreadsheet’s predicted CHE vs LIV scoreline should be changed from 2 – 0 to 1 -1, and the match was still poised at 0-0 at the halfway point before Christensen‘s sending off changed the course of that game.

The most pleasing results for me were in the ARS vs WHU and LEI vs BUR games, both predicted by my spreadsheet to finish 2 – 2.  I am sure most people expected an easy win for The Gunners (hence Aubameyang being the most captained player), and a tight, low scoring affair in the other game. Anyone who saw the first of these two games will know that WHU were unlucky to lose, and to have not scored at least 2 goals themselves. Whilst the high scoring (4 – 2) in the other game ensured my bet on Over 3.5 Goals at odds just shy of 3/1 was a winning one.

Looking at the tables above now, the only result the bookies were nearer to was the last one (WOL vs MCI), but even there, I did point out last week that my spreadsheet actually gave a higher probability to a one nil win for City.

So, for now at least, I will keep faith in the law of averages making a belated appearance sometime soon, and trust in my spreadsheet’s process, even though strictly speaking I can’t fully until around GW17. Turning our attention to GW3 then, here are the number of goals likeliest to be scored by each team.

The first thing to notice with these scores is the absence of any clean sheets, which if GW1 was anything to go by, will no doubt mean a glut of them. As with last week, we have an unusually high number of scorelines here (7), that are deemed paradoxically to have a higher probability than the ones shown above. Namely, BHA 0 MUN 1; CRY 1 EVE 0; WBA 1 CHE 1; BUR 2 SOU 1; TOT 1 NEW 1; MCI 2 LEI 0; and, LIV 1 ARS 0.

There’s a very surprising name at the top of this week’s predicted expected goals table. One of the things my Season Review blog highlighted was the strong attacking numbers BUR posted towards the end of last season, and with their defence in bad shape through key injuries to Mee (and Tarkowski?) at the moment, there’s cause for optimism that they’ll need goals in their upcoming fixture vs SOU. I am personally toying with the idea of bringing in Wood for a one week punt, prior to switching him to Calvert-Lewin.

Having said, there are no clear cut clean sheets chances this week, there are four teams (MUN, LIV, MCI and CRY) for whom conceding 0 goals has a higher probability than conceding 1. These are highlighted in green in the table below.

Now, I appreciate most of you are stanning for EVE assets right now, so please don’t @ me. There’s no denying Palace have been impressive so far, and they did totally nullify the MUN attack you have also been drooling over the prospect of doubling up on beforehand. That said, half of the data used to calculate EVE’s expected goals comes from their very poor post-lockdown form, and before the arrival of Rodriguez, Doucoure and Allan, who have had an immediate positive impact, so I wouldn’t judge you for not heeding my spreadsheet’s warning here.

And finally, the only players deemed likely to average at least 5 FPL points in GW3, not including bonus points, based on their share of their team’s respective xG+xA in the last 8 home or away games, whichever is relevant, are shown in the table below.

Please feel free to ask any questions about the data presented in any of the tables above.

May your arrows be green!

Coley (a.k.a. FPL P0ker PlAyer @barCOLEYna)

FPL Poker Tables GW2

Nothing makes bigger fools out of us than football results can! So much for the shortage of goals and scarcity of clean sheets forecast last week by my GW1 tables. In fact, there were more clean sheets than there normally are when we have a full complement of fixtures! And having predicted a near clean sweep of one-one draws, there were zero! This might sound like a remarkable feat of incompetence, but the probability of such an outcome implied by best bookies odds was 39%. And not because of how outlandish my spreadsheet’s predictions were, by the way. In fact, they corresponded remarkably closely with the probabilities assigned by the bookmakers.

Only one scoreline was deemed less likely by the bookmakers (LIV vs LEE)

In mitigation, the biggest divergences away from my spreadsheet predictions involved the 3 games featuring promoted clubs and, as I explained last week, trying to peg the respective attack and defence strength ratios of promoted teams before the season starts is a guessing game. I also highlighted the ‘known unknown‘ effect of new transfers and, prior to the disappointing performance of Havertz, it seemed as though every single one hit the ground running, and had maximum impact! Think Gabriel, Willian, Wilson, Hendrick, Lewis, Castagne, Allan, Doucoure, Rodriguez, etc.

Understandably, because of the kind of ‘informational disadvantage‘ touched on above, my spreadsheets are likely to be less accurate at the start of a new season. The trouble is that because 8 gameweeks of data for both home and away results represents the optimal data range, we would ideally wait until GW17 before using my spreadsheet’s predictions, but that’s halfway through the season! Instead, we must cling to the hope that the last 8 home and away results from the previous season have some meaningful bearing on the start of the next one. It’s fair to say though that any confidence in this proving so was dented by the opening round of results.

Anyhow, we move; onwards and upwards (not difficult!) to GW2. First off, here are the score predictions, with an added caveat that 7 of these 10 are not actually the scorelines with the highest probability!

Remember the paradox I highlighted last week with regards to WHU vs NEW? And how I explained why 2 – 1 was given as the likeliest score when in fact 1 – 1 had a higher probability? Well, there are not one, but SEVEN such instances this week: EVE 1 – 1 WBA; LEE 1 – 0 FUL; MUN 2 – 0 CRY; ARS 1 – 2 WHU; LEI 1 – 1 BUR; AVL 1 – 0 SHU; and, WOL 0 – 1 MCI. My expectation is that you will find all of these alternative scorelines more plausible, with the exception of the ARS result!

As alluded to last week, there are far stronger candidates for clean sheets this week than last, which pretty much guarantees a complete absence of them if last week is anything to go by! The one that will undoubtedly have people scoffing here is the 61% probability assigned to CHE vs a LIV team that just put 4 past the best defence in the Championship last season.

there are strong grounds for downgrading the P% of a CHE clean sheet to 40%

Please remember though that this is based on The Blues’ last 8 home games and The Reds’ last 8 away and, as discussed in my 2019-20 Season Review, the champions’ form did peter out in the run-in, whereas the fourth place finishers defensive record at home was actually very decent from an xGC point of view. Indeed, their best sequence of 8 home games was better than that of any other team. Shame about the Kepa.

In recognition of the fact that LIV had nothing to play for towards the end of last season, I checked how switching the data from last 8 games only to those for the season as a whole might effect the prediction, and it changed things considerably, reducing the clean sheet probability for the home team to 40%, and making 1 – 1 the likeliest scoreline.

The plea bargaining above should be taken into consideration, therefore, when seeing CHE topping the projected expected goals table below.

CHE would drop to below EVE on 1.6 if using season data rather than last 8

MUN would become the table toppers this week if LIV are given the dispensation discussed above, which is good news for all those planning to captain one of Fernandes, Martial or Rashford, with the last named preferred by my spreadsheet’s expected player points table (see below).

Note that this is not an argument for making Rashford your sole attacking asset from MUN, because next week Bruno will be the MUN player to own. The disparity arises because my spreadsheets distinguish between home and away form. As FPL managers, likely to be owning MUN assets for longer than a one week punt, we are generally interested in overall form, so for the record it is close between these players, and dominance will likely come down to who takes the most pens. Thanks to @AlbertLucue9 for asking the question that prompted this paragraph.

WHU were the big underachievers in last week’s predictions, so scepticism about their prominence here, and that of Antonio below, would be understandable. The other team to leap off the page here is BUR, who as discussed in my Season Review blog finished the season with their underlying attacking stats on a noticeable upwards trajectory. Whether or not they return to type at the start of a new season, with the emphasis back on defensive solidity, remains to be seen.

excludes players from promoted teams

I am in no hurry to repeat the mistake of captaining Antonio again, so I will be entrusting my armband this week to the child poverty champion in the hope that he can feed my team’s hunger for catch up points!

May the flop be with you!

Coley a.k.a. FPL P0ker PlAyer (@barCOLEYna)

FPL Poker Tables GW1

The start of a new season was complicated enough with both Manchester clubs blanking, and the uncertainty around players in our squads going in and out of quarantine against the backdrop of a global pandemic, but the fixture schedule has thrown us an additional curve ball if my spreadsheet predictions are anything to go by. 

Some gameweeks see fewer goals, but the impact on our scores is usually offset (partially at least) by more clean sheet points being available.  However, what my spreadsheet predicts for GW1, is the perfect storm of a shortage of goals and a scarcity of clean sheets.

correct score forecasts for GW1 wth corresponding probability

One-one score draws are deemed the most probable scoreline in all but 1 of the matches, with only CHE and WHU reckoned to be more likely to score more than once (see below). Surely that’s a typo? I must mean abb but 2, right? Wrong. The Hammers fall into a strange paradoxical position I’ve commented on previously. Namely, that whilst it’s true that WHU are more likely to score at least 2 goals than 1, they are less likely to score twice than once. Fried your brain yet? Then might I suggest some lighter reading on quantum entanglement!

predicted xG scores for GW1

In a similar vein, 5 teams are forecast to have a higher than 40% chance of a clean sheet in GW2, whereas only 4 teams are rated to have a better than a one in three chance (33%) in GW1.  Of those 4, only LIV cross the 36.7% threshold, which is significant because that is the point at which conceding zero goals becomes mathematically more likely than conceding one goal.  Remember though, the likelihood of conceding two, three, four goals, etc., also needs to be factored in, so a clean sheet only becomes the likeliest outcome of all when the assigned probability is higher than 50%.

calculated clean sheet probabilities for GW1

There are always caveats attached to my spreadsheets, and rightly so, for algorithms always have shortcomings, but there are additional ones to consider at the start of this season. Obviously, no algorithm can second guess the impact that COVID19 will have on team lineups and results. Likewise the impact of empty stadiums on players and teams.

The other ‘known unknown‘ at the start of any new season is the transformative effect new signings can have on teams’ performances, and one only need reflect back on the arrival of Fernandes in Manchester in January to know how big this can be.

More pertinent to my spreadsheet, however, is the fact that these predictions are based on each teams xG performances in their last 8 home or away games (whichever is relevant), 4 or 5 of which will have come post-lockdown. And, as I showed in my recent review of last season, some teams prospered during this period, while others faltered dramatically.

LIV belong in the latter category, and as they face a promoted team whose defensive credentials in the top flight are yet to be tested (and so can only be best guessed at), it is possible that the probability of a clean sheet for the defending champions is being underestimated by my sheet.

The attacking and defending strength ratios attributed to the promoted teams, based solely on matches against the other six sides in the Championship’s top seven last season, have been scaled back by 20% to reflect the tougher competition they will face in the Premier League.

Unavoidably, this is somewhat arbitrary, but not random (never random!). Rather it is based on some research I’ve read, but I accept that it’s an inexact science. The point being that if you believe the transition from EFL to PL is more than 20% harder, then the probability of LIV keeping a clean sheet increases accordingly. For what it’s worth, the probability of LIV keeping a clean sheet only increased by 3% to 42% when I substituted the figures for the season as a whole, rather than taking their last 8 games only.

That all said, it’s quite conceivable that LEE will be like rabbits caught in the headlights of an oncoming juggernaut, and an early goal will open the floodgates, and demoralise them. Personally though, I’m not banking on it.

Bring on the new season, bring on the green arrows, but most of all, bring on the coronavirus vaccine!

Coley a.k.a. FPL P0ker PlAyer

2019-20 Season Review of my spreadsheet’s rolling 8GW data

During the course of last season, I established that an 8 gameweek data was optimal for my spreadsheets.  From GW17 onwards, my spreadsheet’s better than bookies odds predictions were based on each team’s last 8 home or away games, whichever was relevant for the upcoming gameweek.

Recently, I found myself wondering to what extent teams’ attack and defence strength ratios fluctuated throughout the season.  And I wondered if looking more closely at how these values changed would reveal which teams showed the most improvement post lockdown.

So I coloured each team’s best to worst 8 game sequences on a scale of blue to red, and was pleased to discover it was easy to see where in the season each team’s best and worst xG form was from the start of the season to the finish (left to right).  In the end column I highlighted in blue the teams that performed to their best levels in terms of xG during the run in.  The table below is how it looked for xG expected to be scored at home vs an average defence.Rolling xG HDisregarding relegated BOU, this table suggests ARS BUR CHE LEI and SOU finished the season strongly in terms of attacking threat at home, but it is important to understand that the blue to red scale was applied to each team separately.  The table below is the same as above, but with the same scale applied to all teams relative to each other.  Now we see that we should not perhaps be getting too carried away with the attacking strength of ARS and SOU at home just yet.Relative xG H


xG H rankingsNext I ranked each team according to their highest, lowest and average (mean) ratios. The table to the right shows these rankings for xG expected at home.

As can be seen, CHE were a force to be reckoned with at home. Their best 8 gameweek attacking spell at Stamford Bridge was bettered only by MCI, whilst on the defensive side of things (see table below), the xG they conceded in their closing 8 home games ranked as the best 8GW sequence recorded by any team last season. Away from Stamford Bridge they ranked only 5th and 10th best respectively.

WOL ranked 4th best in terms of their xG ceiling at home, but drop to 8th place with regards to their xG floor. This does not account for why they finished behind TOT in the league, however, as they bettered Spurs in every category (highest/best, mean, lowest/worst) for xG scored and conceded both Home and Away.xGC H rankings

CRY were a top 8 side for keeping things tight at home (see left), but this might be a euphemistic way of describing their lack of attacking threat at Selhurst Park, which ranked 2nd worst across all 3 categories (see above).

Surprisingly, BUR‘s best attacking spell at home, during the second half of the season, ranked 6th highest in the division (see above), but appears to have come at the expense of the defensive side of their game. Certainly, they consistently conceded less xG in the first half of the season (see below).


Rolling xGC H - BURLEI‘s game appears better suited to playing away from home. The Foxes’ best 8 gameweek attacking spell on the road ranked 2nd best in the league, and the xG they conceded during their worst sequence of away games was comparably better than any other team managed.  On average, they had the 3rd best away attack and 4th best away defence (see below), whereas at home these only ranked 13th and 9th best.

xG & xGC A rankings

The overlapping between different teams’ highest, average and lowest ratios can be better visualised in the following charts.  First off, here’s the 8GW sequences of xG at home.  The number on the Y axis represents the number of xG that teams would be expected to score vs an average defence.Home ATTACK

And here’s the 8GW sequences of xG expected to be conceded at home vs an average attack.Home DEFENCE

Another piece of information conducting this exercise provided me with was the difference between the average number of xG teams would be expected to score and concede vs average opposition.

Strangely, only 7 teams had a positive average xG difference at Home. In descending order these were MCI LIV CHE MUN WOL SHU and EVE.  Counterintuitively, 9 teams had a positive average xG difference playing Away.  These were MCI LIV LEI WOL EVE MUN SOU CHE and ARS.

Of the teams above, only LIV and SHU were not amongst the teams highlighted as having strong finishes to the season in terms of attack and/or defence at home and/or away.  The inclusion of EVE in the ranks of strongest finishers (Away defence) is the biggest surprise here, but can probably be explained by the fact that half of their last 8 away games came before lockdown.  If EVE can recover their pre-lockdown form, however, their defenders might represent good value if FPL Towers base their price points on the poor form shown after the Restart.

Hopefully, the insights gained from this exercise, combined with an accurate assessment of the difficulty of fixtures faced by teams at the start of the 2020-21 season, can give us a flying start.

Good luck, everyone!

Coley aka FPL Poker Player @barCOLEYna



Using baseline BPS to identify backline bonus magnets

I had another FPL itch recently that I needed to scratch.  I had an idea for a more nuanced approach to using bonus points system scores to better identify from which teams the goalkeeper was the best pick for our FPL squad defences.

There will be few FPL managers who’d have argued against Pope being the best BUR option last season, for example, but what about other teams?  Are goalkeeper always the pick of the crop when it comes to clean sheet bonus points?

It won’t surprise you to know the answer to that last question is an emphatic No!  In fact, the results from my subsequent research showed wide variance in BPS points earned by goalkeepers last season when clean sheets were kept.

My idea was to compare the BPS points that defenders and goalkeepers earned whenever their team kept a clean sheet.  To my way of thinking, it is more meaningful to analyse how these players fared only when successfully doing so, because for them that’s generally when bonus points are up for grabs.  These tight-at-the-back games are also more likely to be a better predictor of future bonus points allocation than when we also include the noise from matches when teams are chasing the game.

As well as filtering for the games players kept clean sheets, I also removed the effects of goals, assists and yellow cards from their BPS scores to establish baseline averages.  There are obvious exceptions, but generally speaking it remains the case that goals and assists are rare events for most defenders, and clean sheets are much more predictable than attacking points are.  Again, some defenders are known card magnets, so this will still need to be factored in when using the results of this exercise to determine future transfer targets.

So, without further ado, here are the table toppers who banked a minimum of 5 clean sheets last season.  Firstly, the 20 players who average a baseline BPS of 27+ per clean sheet:


Top 20 baseline BPS

As you can see, only 4 goalkeepers (highlighted in lime green) feature in this top twenty, which is consistent with most defences’ ratio of defenders to keepers.  These were Lloris, Pope, Guaita and Leno.

The players with under 8 clean sheets are also highlighted (light red fill dark red text).  Small sample size notwithstanding, Cancelo already looked the best route into the MCI backline, even before 2 recent UCL ties suggested he is now Pep’s preferred choice for the left back position.

Gomez being best for baseline bps for the league champions came as a surprise to be honest, but I don’t see his slight edge over Alexander-Arnold here being enough to change many FPL managers plans!

High BPS scores are all well and good, but they do not in and of themselves count for anything unless accompanied by Bonus Points, so the next table shows the top 20 players for average bonus points collected per clean sheet:

Top 20 average bonus

This time there are 6 goalkeepers in the top twenty, with Henderson and Ryan both entering the frame.

I read somewhere recently that in terms of of BPS per 100 mins, Ryan was the best option from the BHA backline, but my method shows that Dunk is actually the standout pick.  He averaged over one and a half bonus points on the 9 occsions he was involved in a clean sheet.  Intriguingly, there has been speculation linking CHE with Dunk, a boyhood fan, and he could be a real bargain for FPL managers if BHA agree to sell.

Luiz being next best of the defenders will no doubt come as a shock to those who witnessed many of his lamentable performances for ARS last season, but we should be wary of our cognitive biases blinding us to his potential bonus point magnetism, which averaged 1.5 on 8 occasions last season.

Interestingly, EVE have more representation here than any other team with 4 defenders making a fifth of the top twenty.  This might merely be a reflection of how little cutting edge they have at the other end of the pitch.  Sidibe‘s prominence here probably owes much to him being fielded out of position as a right midfielder on a number of occasions, so Keane might have been the best of the bunch even if the former’s loan contract at EVE had not expired.

I imagine most managers would have expected Dubravka to be the clear choice when it came to NEW assets, but my method points to Fernandez being a better option.  Certainly as far as collecting bonus points is concerned.

Chilwell and Digne both average more than a bonus point per clean sheet, and for half a million less, the former could depose Azpilicueta as the primary beneficiary of bonus points when CHE do keep a clean sheet if sealing a move to Stamford Bridge.  Unless Dunk transfers there too of course!

Mina and Van Aanholt are the only other defenders in the 1BP+ members club but there are better (and cheaper) bonus point magnets in their respective teams.

Another thing to notice in the second table above is how there are no represantatives from the teams who finished in the top 3 places.  This highlights one aspect of owning defenders from top teams, which is that they will struggle for bonus points in the absence of attacking contributions, because these teams score more goals, and BPS generally favours goal scorers and assisters.

Remember though, the purpose of this exercise was to identify the teams where the goalkeepers were the best option.  Seemingly, the only teams where this was the case, from the point of view of bonus points at least, were TOT, BUR, CRY, SHU and SOU, in the form of Lloris, Pope, Guaita, Henderson and McCarthy, respectively.

There has been much debate about whether Pope still represents good value at 5.5m, but only Lloris, with a much smaller sample size (6 clean sheets vs 15), is ranked higher than him in the tables above.  This remains the case (see below) even if we factor starting price into the equation by using an average Bonus Points per £m metric instead:GK average bonus per £mPope does drop down to 7th though, with Ryan and McCarthy 1st and 2nd, when ranking keepers by baseline BPS per £m, but as stated previously, BPS are meaningless if not accompanied by bonus points.

The only defender who can better the 6bps per £m of Matt Ryan is another Ryan.  Namely, Ryan Fredericks of WHU (see beloe) who manages 6.1, so he is another name to keep in mind if fixtures fall kindly for the Hammers.

Top 10 DEF baseline BPS

Where it matters most though, Dunk is once again the star defender when it comes to average bonus points per clean sheet per £m starting price:

Top 10 DEF average bonus per £m

Keane, Fernandez and Mings are the other names to join Dunk and Fredericks in both these top tens, and might be worth keeping in mind when the fixtures are released.

Ultimately though, these metrics I’ve used are only useful if the players under the spotlight here are again involved in clean sheets this season.  Furthermore, there are other considerations than just the ones focused on here when it comes to selecting our initial squad, such as team structure and balance.

I am glad I scratched my FPL itch though, because I feel like I have greater clarity now about which goalkeepers and defenders to include in transfer considerations throughout the first half of the upcoming season.

Hopefully, you’ve found the contents of this article interesting food for thought too, and whether it informs your squad selection or not, I wish you all the very best for the season ahead.


Coley (aka FPL P0ker PlAyer)







What FPL and Poker have in common


It always makes me smile when FPL managers express the hope that others will have a good gameweek.  It reminds me of the way poker players wish each other good luck” when strictly speaking that’s the last thing they should want.  In reality, they usually don’t care much whether or not your luck is good, just as long as it doesn’t affect how much they win.  Similarly, when FPL managers say “may your arrows be green”, they usually don’t mind if they are, just so long as theirs are greener!

But that’s okay because the world is a better place for such niceties.  Less friendly are the passive aggressive congratulations given to players who have clearly ‘lucked out’.  So in poker, aggrieved players often say nice hand or well played to their arch-nemeses with barely suppressed sarcasm.  The same dynamic is also seen sometimes in replies to FPL tweets when screenshots of lucky autosub outcomes are posted.

One of the most obvious similarities between the two worlds is with the abundance of ‘bad beat’ stories.  In poker, these are told by victims of perceived misfortune.  In FPL the same kind of hardluck stories are seen all the time regarding either the big points scored by recently transferred out players, or the big points missed on a coin-flip decision.  Deep down we all know it’s wrong to inflict such self-pity onto our peers, but it’s a compulsion we find tough to resist.

The bête-noire of poker etiquette, however, is the ‘slowroll’, where someone leads you to believe that you have won a hand, but then turns over a very strong hand.  The FPL counterparts of these offenders are the grandstanders who, having amassed an impressive gameweek score, tweet to ask everyone how many points they’ve scored before then revealing their own better one.


MatrixThe correct answer to most questions posed by poker novices is “It depends.”  The same is true in FPL.  Most “Should I transfer in Joe Bloggs for John Doe?” type questions would benefit from a similar precautionary principle being applied.

For example:

Do you have more pressing problems elsewhere?

Where are you in your Mini-League(s)?

How does your squad compare to your ML rivals?

How will future transfer plans be affected?

These are all valid questions to ask before giving yes or no answers.  In poker, this is like being asked how to play a specific hand in early position in a tournament, where the comparable “It depends” type answer might include the following preliminary questions:

Are you nearing ‘the bubble‘, i.e., the prizemoney?

How many chips do you have in relation to the other players at your table?

Will you have enough chips left to make a move later if losing the hand, e.g., stealing the blinds from passive players to your left?


One hallmark of a good poker player is discipline when it comes to bankroll management, and most good FPL managers exercise budgetary discipline also.  A bad one makes kneejerk transfers to bring in premium priced players with little regard for no longer being able to afford to replace or upgrade any of their other out-of-form squad members.

Clearly, there are aspects of both games that are beyond our control.  In poker it’s the cards we are dealt, and in FPL it’s events like injuries and suspensions.  All we can do is try to play optimally in the areas within our control.

One such area is the weekly free transfer.  A FPL ‘shark‘ knows these should always be used to increase ‘expected value‘, primarily in terms of points, but also with regard to team value.  The ‘fish‘, however, is more likely to make kneejerk moves, that leave them wishing they’d hung fire, and/or maverick moves that usually share the same fate as those made by poker players guilty of ‘fancy play syndrome‘.


calculated riskThere is a tendency in the FPL community to describe any transfer not conforming to template as maverick, but there is a distinction to be made between maverick moves and calculated risks.


Few things in poker are more maddening than being ‘rivered‘ when ‘all in‘ as a massive favourite to win a hand, only for the final face-up card dealt to be the one and only card that gives your opponent a better hand.

poker skill and luck

The same frustration is felt in fantasy football when head-to-head or mini-league rivals hit the FPL equivolent of such ‘one outers‘ by having an autosub goalkeeper (Gomes in 2015-16) make two penalty saves in the same gameweek say, or an autosub outfield player score a brace on a Monday night.

Bad players of poker and FPL will frequently ‘suck out‘ on us, and be rewarded for poor game management, but the good news is that being rewarded for playing badly only encourages them to go on playing badly!

All poker players and FPL managers will experience ‘downswings‘.  What seperates the wheat from the chaff is how well they cope mentally with an inevitable aspect of the game.  In poker, most players are prone to going ‘on tilt‘ when they experience bad luck.  Such players are as a result more likely to lose money because they’ll be making decisions based on anger and frustration.  In FPL the same can be true if playing in cash prize mini-leagues.


Last season, for example, I was so filled with self-loathing about my failure to confirm activation of my Bench Boost chip in GW34, thereby costing me 43 points, that I self-destructively wasted my Triple Captain chip the next gameweek by gambling on serial cameo appearer Coutinho (1 point!).

Admittedly, I was comfortably winning my main money mini-league at the time, but my fog of despair was so dense that a few weeks passed by before I realised I’d sabotaged any prospect of cashing out in any of my other money leagues.  I’d been knocking on the door of the top one thousand club for several weeks, and those 43 points would have seen me well and truly kick the door down!


Moneymaker The way FPL has evolved over the past few years reminds me of the sea change that happened in the poker world after 2003.  There was a sudden growth in interest in poker after the $2.5 million first prize World Series of Poker Main Event was won that year by Chris Moneymaker, a 27-year-old accountant and amateur poker player from Tennessee, who had won his seat into the event through a $86 satellite tournament in a PokerStars online poker card room.  The ‘Moneymaker Effect‘ would give rise to a new breed of internet poker players who honed their skills online before facing off against old-school competitors.

Doyle v Durrr

The poker player stereotypes that had been around for an age were transformed over the course of a decade. The old-school competitors who believed in poker being an art, not a science, based on feel, instincts and reads, were quickly overrun. A new breed of math nerd, wonks using a mountain of sortable data from the millions of hands played online began to dominate the game. Math whizzes changed the game using probability theory to their advantage.

Similarly, I think the generation of fantasy football managers, who believed that football knowledge and a ‘good eye’ were the only requirements for success, are in the process of being overthrown by algorithm builders, probability theorists and statisticians.

Vanessa SelbstConsider yourself warned if you’re one of those who underestimates the relevance of maths in FPL. That geeky person who invited you to join his or her money mini-league is probably hustling you!

BlessedI think there might have been a typo in Matthew 5:5; perhaps it should have read:


Blessed are the Math gEEKs!

Good luck at the tables y’all!


FPL Poker Player @barCOLEYna

If Carlsberg did FPL spreadsheets….

They would probably be like mine.

Some spreadsheets simply track each team’s fixtures in gameweek order.  Some incorporate fixture difficulty ratings, with the aim of identifying favourable/unfavourable sequences to assist with managers’ transfer decisions. Although, as Richard Kenny @InfernoSix showed, FDR is a very blunt instrument when it comes to assessing fixture difficulty.  And some spreadsheets even assign predicted FPL scores to players based on a wide variety of algorithms, including RMT.  Mine do many of these things and more, but probably better.

Cheat Sheet

How I Got Here

I first came across the concept of season tickers during my first proper FPL preseason.  I immediately liked the concept, but as my second season progressed I grew dissatisfied  with what I perceived to be the arbitrary nature of team rankings, and distrustful of how often they’d be updated to reflect new realities.  Having finished 17,394th in my second season, I believed there was plenty of scope for improvement, and so I set out to learn what I could do with my desktop excel program, which I’d never had any use for previously.

I went into my third season determined to fly solo and rely solely on my own formulations.  With no formal background in maths whatsoever, I intuited a method of calculating the offensive and defensive strengths of each team relative to all the others.  As my Excel know-how grew I had several Eureka! moments that led to my second blog (December 2015) proclaiming my first prototype of a season ticker spreadsheet based on what I would later come to understand as a crude form of xG, though I’d never heard of that concept at that point.   My spreadsheets were constantly being refined throughout that season, but I nonetheless achieved a new personal best finish of 12,599th.

Unfortunately for my average overall rank though, I cut corners the following season by substituting my homegrown variety of xG with the much less labour intensive, and more readily available, Shots On Target data.  This was considered by many FPL managers at that time to be the most important metric of all for predicting attacking returns.  See One Stat To Rule Them All for example.  My complacency and laziness proved disastrous, and I did well to finish inside the top one hundred and fifty thousand (148,327) having been still outside the top two million going into Gameweek 12.

When I first met David Wardale @DavvaWavva during the summer of 2017 to be interviewed for his excellent book about fantasy football – Wasting Your Wildcard – I was feeling very bullish about my fifth season prospects.  This wasn’t the same blind optimism most players of FPL experience before the first bunting of red flags or first quiver of red arrows.  No, my optimism was based entirely on my discovery of a reliable source of xG data.  I’d done tonnes of research into the many different models used to calculate xG, and had settled on a source I trusted to take my spreadsheets to the next level.

In the course of that research into xG models, I’d been very pleased to learn that my homegrown method was almost identical to that now used by many professional sports bettors.  This was also when I learned of a concept called poisson distribution, which came to revolutionise my understanding of most likely correct scores based on my spreadsheet calculations, but more on that fishy sounding concept later.

Where I Am

When deciding where to ‘invest’ their money professional sports bettors use websites like FiveThirtyEight and ClubElo.  These were reckoned to be amongst the very best last season by alex b @fussbALEXperte who, apart from writing excellent articles about football and psychology, also measures the quality of football predictions.  After taking a close look at the methods and principles used by FiveThirtyEight and ClubElo I was pleased to see that they are essentially the same as mine.  Coincidentally, the latter was also credited by the aforementioned Richard Kenny as offering a more reliable barometer of teams’ attacking and defending strengths than FDR.

As far as I am aware though, these sites focus on upcoming matches only, which makes perfect sense given that most of the bettors they cater for have little interest in betting on the result of league matches several weeks away.  After all, there are a multitude of variables that can change teams’ future prospects in the meantime, e.g., injuries, morale, suspensions, transfers, etc.

Catering for FPL managers is a different ball game, however, as they must plan ahead to be successful.  They only have two wildcards per season and one free transfer per week, so my spreadsheets are just as focused on long-term projections about teams’ forthcoming fixture runs as they are for more immediate short-term predictions.

What I Do

After each gameweek I carefully enter the expected goals scored and conceded values reported by my preferred source, for all of the matches in the latest round of fixtures, into pre-prepared cells on my spreadsheets.  Consistently applied adjustments to these values are made in certain circumstances, e.g., red cards, unfairly disallowed goals, missed penalty kicks resulting in follow up shots, etc.

These are then systematically weighted to give every team individual values for strength in attack and defence, for both home and away.

The rationale for distinguishing between home and away form is simply that many teams approach away games differently to when playing at home, often changing their formations and personnel in the process.

The sophisticated part comes next, when my algorithms model what my subsequent spreadsheets will look like if, and admittedly it is a big if, my spreadsheets’ current predictions correlate precisely with actual outcomes.  Clearly, this is never going to happen, but I do find these dynamic xG projections to be more reliable than static ones that assume the status quo will remain relevant.  In effect, my sheets anticipate the cascading effects of predicted new data being added, and redundant data being subtracted.

xG GW25-30

The analogy I use is modern weather forecasts, and how they are based on computer simulations that evolve the state of the atmosphere forward in time using an understanding of physics and fluid dynamics.  They attempt to predict what the weather will be in the future, not what it is now.

Extrapolating from the values my spreadsheet assigns to every team allows me to do many things, including sorting teams by the predicted number of expected goals that will be scored and/or conceded over any number of future gameweeks for any range of gameweeks desired.  It also enables me to produce weekly correct score predictions.

An important distinction to make here is that what my spreadsheets predict is ‘expected goals‘, not actual goals.  As can be observed in the match stats shown during Match Of The Day post-match interviews with club managers, these often don’t correspond with each other.

Before coming to the vexed question of what the point of generating theoretical goals is if they can differ markedly from goals scored in reality, I should add that over the course of a season there’s usually very little to separate the total number of goals scored in the Premier League from those expected by the xG model I use.  Last season, for instance, total expected goals for all teams only exceeded actual goals by 46, which works out as an average of under 0.1 of a goal per match per team.

Why xG? 

Have you ever watched a game of football and seen the team with the biggest chances to score goals lose?  Then there’s your answer.  Before I started playing FPL, unfortunate events like calamitous errors by individual players, poor refereeing decisions, and unlucky deflections, were all just grist to the opinion mill.  It was only when I started recording match results onto my spreadsheets, and saw the distorting effect such simplistic data had on my team rating equations, that I came to realise all goals ought not to be accorded equal significance.

After 7 matches last season, Crystal Palace had yet to score any points or goals.  By the standard measure of actual goals they were destined to drop down into the Championship.  Clearly, they were a team who couldn’t score goals, and on that basis, wouldn’t score the goals needed to avoid relegation.  The Expected Goals model, however, told a different story.  One in which they were cast as merely the unlucky victim of variance who had experienced the unfairest results up until that point.  And the villain of the piece was our old friend ‘standard deviation‘.

Even before I came across xG on my fantasy football travels, my homegrown variety had me start Leicester’s title-winning season with Vardy and Albrighton in my FPL squad (alas not Mahrez), at a time when they were very unfashionable picks.  My DIY xG spreadsheet had identified Leicester as a vastly underestimated team judging from performances during their ‘great escape’ the season before.  I remember well my increasing exasperation with radio and television pundits alike midway through that season as they all declared relegation for the Foxes a foregone conclusion.

These, and countless other examples besides, have led me to confidently conclude that xG gives a more accurate picture of the attacking and defending strengths and weaknesses of teams than do the Goals For and Goals Against columns in a league table.

Predictably enough though, there are still large swathes of the FPL community yet to embrace this revolutionary way of understanding football matches.  My twitter feed is still littered with uninformed commentary and misguided sarcasm, from some of the most followed twitter accounts, at the expense of the xG approach.

Flat Earth in space with sun and moonTo my mind though, these snipers and swipers are akin to ‘flat earthers’ denying the earth is round.  I expect Tony Bloom and Matthew Benham wouldn’t have any sympathy for these conspiracy theorists either, given that they were able to buy football clubs (Brighton Hove Albion and FC Midjylland & Brentford) with the proceeds from their xG-based sports betting operations.

What’s The Plan? 

I’ve been refining and perfecting my spreadsheets for several years now, and they’ve helped me to sub-twenty thousand finishes in three out of the last four years, the last of which was a decent 4,733rd.

What I’ve enjoyed most about letting my xG spreadsheets govern my decisions is that they often promote going against the grain of template teams, against the flow of groupthink, and against the tide of crowd wisdom.  And yet, the maverick moves I’ve made have generally kept me ahead of the curve.

The time and effort I put into my spreadsheets has sometimes been hinted at in screenshots I’ve shared on social media posts, but the time has now come for me to make them available to other FPL managers.

The elusive nature of form in football, and the sensitivity of my algorithms, means that fluctuations in my player and team ratings are inevitable, but if my spreadsheets perform well, these should be gradual rather than volatile.

My spreadsheets will not eradicate difficult decisions regarding captaincy and transfers, however, and should be used alongside managers’ judgement and knowledge, not instead of them.  Getting the most from my sheets will depend greatly on synthesising them with managers’ instincts.  The onus will still be on managers to make adjustments and allowances for events like key injuries (and returns from injury), suspensions, transfers, etc., the significance of which cannot be immediately captured by my spreadsheets.

Reality check 

The most common scoreline in the Premier League last season was one-one, which happened just under twelve percent of the time (11.84%).  The next most common score was one-nil (11.58%).  And then there were just as many nil-nil draws as there were two-one wins (8.42%), meaning that in just under a third of all matches (31.84%) neither team scored more than a single goal.


Using a poisson distribution applet I was able to calculate that the most probable that a 1-1 scoreline could ever be is 13.53%.  That’s longer than 6/1 in fractions, but shorter than 13/2.  Accepting any odds of 6/1 or less (11/2, 5/1, 9/2, etc.) for a 1-1 score draw, therefore, would mark you out as a ‘mug punter‘.

The implications for successfully predicting scorelines are considerable.  Even if we find 10 matches on a football coupon that all had the highest probability of a 1-1 scoreline possible, the chances of getting at least 3 out of the 10 correct will never exceed 29.75%.

In other words, don’t be calling my spreadsheets out if they only get one or two score forecasts correct each gameweek, because in  reality, achieving 3 out of 10 with any more regularity than once every three gameweeks is against the odds.

As for correctly predicting all the results of the free-to-play SkySports Super 6, you will never have higher than a 0.00061% chance of winning the jackpot.  That’s a one-in-one hundred and sixty two thousand, seven hundred and fifty two chance (162,751-1), in the best case scenario.  Little wonder then that I’ve never won it!

If you were in any doubt about the random nature of much of what happens on a football pitch, then these startling odds should help you better understand the enormity of the task faced by those trying to provide accurate predictions.

If you understand predicting scorelines is difficult, then you will realise that forecasting who will be doing the scoring and assisting can be an even more unpredictable business.  Like weather presenters assuming the weather tomorrow will correspond with that of yesterday, much of what passes for FPL punditry too often presents evidence of what has happened in recent gameweeks as incontrovertible proof of what will happen in future gameweeks.  In my experience though, such simplistic thinking in FPL rarely work out how we expect it to.

Health warning 

Finally, I should warn you about the dangers of dependency.  Use of spreadsheets can become seriously addictive.  Never binge drink on them as they can really go to your head.  And they might give you dutch courage to make maverick moves that leave you with a really bad overall rank hangover.

Please drink responsibly. 


Coley aka FPL Poker Player @barCOLEYna





2nd session chip counts are in

Okay, so we’re about to enter the third session of the 9 month long poker tournament that is FPL, and I thought I’d make a diary entry of my promising progress so far.

Gus Hansen recordingThis post is inspired in part by poker player Gus Hansen (left), whose book Every Hand Revealed about winning the 2007 Aussie Millions World Poker Tour main event chronicled in real time his progress from the beginning of Day 1 to the end of heads-up play on the final table via notes he spoke into a handheld recorder throughout the tournament.

On the off chance that this proves to be my year in the World Series of FPL, I figure it’ll be handy to have kept a blog, because apart from anything else, it’ll make answering all those questions in end of season interviews so much easier, right?


So, what’s the story so far?  Well, I had a good second session, building my chip stack up to 446 points from 170, and moving up the overall rankings from 602,989 to 23,420, placing me inside the top 0.5% of entrants.  I’m yet to play any of my special chips (Bench Boost, Triple Captain, Free Hit, Wildcard), and my realisable team value is £100.6m (£101.7 at current prices).

The field is still huge, however, and there’s quite a ways to go before I can start dreaming about making the ‘final table’.  As ever, it’ll only takes a few ‘bad beats’ to completely derail my attempt to go deep in this tournament.

I’ve adhered thus far to the ‘tight is right‘ philosophy subscribed to by many successful tournament players.  That is to say I’ve played conservatively and avoided putting my chip stack at risk early on.  I’ve been patient with my hands and kept gambles to a minimum, with only 1 hit to date.  While others quickly lost confidence in their gameplans and ‘spewed chips’ in kneejerk fashion, I held my nerve and kept faith with the strategy of waiting until I got a good read of my table.  As with poker tournaments, and the Premier League itself, you can’t win FPL in the first few weeks, but you can lose it.

Obviously it helped that unlike last season I’ve ‘run good’ early doors with regards to avoiding ‘coolers’ or ‘setups’ (injuries and suspensions).  That said, I’ve endured a couple of injuries, a couple of penalty misses (Lukaku & Vardy) and a missed tap-in (Vardy again).  Nevertheless, I’ve accumulated chips steadily with most of my captain picks, and ‘defended my blinds’ successfully with shutouts aplenty.

In fact, whereas it took me 11 gameweeks last season to muster 4 clean sheets, it only took me 4 this time around to garner 11!  Contrary to received wisdom, good defence has proved to be the best offence for me (3 goals and 5 assists) and the cornerstone of my campaign so far, with 22 clean sheets banked already, including 7 out of 7 from my goalkeepers.


sneak a peak at pair of nines

My progress up to now has been achieved without being dealt any of the bigger hands.  I’m yet to be dealt either of the biggest pairs (A,A or K,K), more commonly known in an FPL context as Aguero(C) or Kane(C).  Instead, my biggest gains so far have been courtesy of ‘medium pairs‘ like the 9, 9 (a.k.a. Captain Lukaku) I rivered a goal and an assist with against Everton, ‘small pairs‘ like 3,3 (a.k.a. Davies) with which I’ve already won three double figure pots,

Eriksen Wheel Straight Flush

and ‘small suited connectors‘ like the 2,3 (a.k.a. Eriksen) I made a straight flush with against Newcastle.

Probably the best hand I played though was during the 4th Level, when I made a disciplined fold with my DDGout-of-position‘ against an aggressive opponent by the name of Stoke, and played my Elliot against a passive Swansea instead.  That round of hands also saw my patience with ‘suited aces‘, De Bruyne (ace, seven) and Kolasinac (ace, three) belatedly rewarded with a couple of small pots of 9 and 11 chips respectively.



A7 and A3







Naturally, there have been some missteps along the way, and I’ve lost some pots I should have won, most notably when I gave up on my Jesus hand too easily, folding to a bluff from Guardiola in the 3rd round of betting.  Pep succeeded in putting me a little ‘on tilt’ thereafter, because I compounded my error by ‘overplaying’ Chicharito twice in the next 2 levels.


With 7 gameweeks played, however, I now have a ‘good read’ on my table; my season-ticker spreadsheet table that is!  All teams have now played at least 3 games at home and 3 away, meaning I can finally dispense with last season’s numbers.  From now on my expected goals predictor will rely on adjusting and weighting this season’s results only.


To me, my spreadsheet feels like an FPL equivalent of the ‘push/fold’ chart I use when playing online poker tournaments.  Essentially, such charts tell me mathematically whether or not going all-in with a particular hand in a specific situation has positive expectation.

By the way, these ‘cheat sheets’ are prohibited during hands in live poker tournaments such is the advantage they are deemed to bestow.  See below for a very entertaining hand from last year’s World Series Of Poker Main Event for confirmation of the saying “cheaters never prosper“.

For what it’s worth, McConnon’s chip stack was a little too large in my opinion for his chart to be relevant anyhow, but certainly he was bordering shove-or-fold territory.

Thankfully, FPL decisions don’t also have to be made in a live arena without recourse to tables, tools and spreadsheets.  For the remainder of this season then, the overwhelming majority of my FPL decisions will be governed by what my spreadsheets tell me with regards to how many goals teams are expected to score in forthcoming fixtures, as well as how many clean sheets teams are expected to keep.

With this better read on fixtures and form, my plan is to gradually widen my ‘open raising range’ and make more aggressive moves than hitherto in a bid to acquire a dominant position in the tournament.  The aim is to reach a powerful enough position to be able to apply ‘leverage’ and force opponents to worry about my captain choices and transfers, and how best to counter them, not vice the versa.

Somewhat paradoxically, I’m willing to gamble more now in the hope of  being in a position to gamble less later.  After all, it is much easier to accumulate chips from a position of strength as a big stack bullying small ones, rather than from a position of weakness as a small stack.


So then, which teams does my cheat sheet recommend folding, calling or raising with during the next few levels?  Extrapolating the likeliest scorelines over the next 6 gameweeks from my expected goals spreadsheet, I strongly suggest not attacking with hands that contain Brighton, Crystal Palace, WBA, Burnley and Huddersfield cards, and raising with ones that have Spurs, Man City, Everton and Leicester cards in them.  At this stage, Chelsea cards are surprisingly only considered okay for raising with during the next 2 rounds, and only calling with thereafter.

GW8+ extrapolations

Hands with ‘blockers‘ from West Ham and Man City are deemed the best with which to ‘defend blinds‘ with over the next 6 levels, while those from Bournemouth, WBA and Watford are considered the worst, spelling trouble for Foster and/or Hegazi owners.  The prominence of West Ham on my clean sheet predictor will surprise most people, and does have the look of an outlier here, if not downright anomaly, but I hope not as I traded Hegazi in for Cresswell with my GW7 free transfer.


So there you have it, the third session is about to get underway and will consist of four more gameweek levels before the next international break takes place.  I hope you’ll join me then for my next update.  In the meantime.. may the flops be with you!

Coley a.k.a. FPL Poker Player @barCOLEYna



bad beats  subjective term for a hand in which a player with what appear to be strong cards nevertheless loses

blockers  – holding one of the cards your opponent needs to complete their hand (goalkeepers and defenders)

cooler  – situation in which a player holds the second best hand, so strong considering the circumstances, that they are apt to lose the maximum with it no matter how they play it (Mane red card, Aguero car crash, etc)

defending blinds  – call or raise an opponent’s raise when in the big blind, rather than folding an otherwise weak hand, in order to exploit overly aggressive players (clean sheets)

final table  – last table in a multi-table poker tournament. The final table is set when a sufficient number of people have been eliminated from the tournament leaving an exact number of players to occupy one table, typically no more than ten players

good read  expectation of what hand an opponent might have (large enough sample size of data)

leverage  – the threat of facing bigger bets on later streets, which can be enough to motivate a fold right away (forcing opponents to punt on differentials)

out-of-position  – players “have position” on opponents acting before them, and is “out of position” to opponents acting after them.  Because players act in clockwise order, players “have position” on opponents seated to their right, except when opponents have the button (away fixture)

overplay  – to invest more money in it than it is worth

push/fold  – reducing pre-flop options to either moving all-in or folding your hand

setup  – situation where two players had no choice but to get it all in

spewing chips  – generally trying to fight for every pot, which usually doesn’t end well (jumping on and off every bandwagon and sinking ship)

Deviation is standard in FPL

Pair of Agueros

Poker mirrors FPL in many unsuspected ways.  At the heart of both games are strategies to maximise the accumulation of points.  It’s just that in poker, the points are in the form of chips.  In tournament poker and FPL we have to keep in mind how our chip stack or total score is faring relative to others on our table or in our mini-league, as well as compared to those on the overall leaderboard.

Understanding the strengths and weaknesses of hands is as important in poker as it is with our squads in FPL.  Knowing whether a hand plays better in raised pots, heads up against single opponents say, rather than limped ones multi-way, is a little like weighing up whether transfers help or hinder our planned orientation to a 3-4-3 or 3-5-2 formation.

In poker, it’s really important to strike the right balance between ‘value betting‘ and bluffing.  Otherwise, we’ll be pegged as easy-to-read, fit-or-fold type players, and we’ll never get paid-off when we have a big hand.  This brings to mind the balance we need to strike between template players and differentials in FPL.  After all, it’s difficult to make any headway in our mini-leagues if we’ve got pretty much the same team as those above us in the standings.  Having too many differentials is akin to the rookie error of calling bets on the ‘flop‘ and ‘turn‘ chasing a ‘runner runner‘ ‘gutshot‘ straight draw, which relies on improbably catching perfect cards on both the turn and the river.

Another balance to strike in poker concerns the careful sizing of bets that keep as many options open to us as possible depending on what our opponents do next.  This translates to FPL in terms of the spread of player price ranges within our squad.  It is a balancing act we must perform well if we are to succeed, and relates to the proportion of our budgets we allocate to different positions within our squads.  Going uber-cheap on defence, for example, by starting the season with five 4.5m-or-under defenders runs the high risk of our squads requiring immediate surgery should the GW1 lineups feature few if any of our bargain basement buys.  Having a good balance of price ranges insures us against unforeseen circumstances such as bans and injuries.

An undeniable fact of FPL is that the so-called big hitters will sometimes blank, just like the best hands in poker (pocket aces, pocket kings) are often busted by lesser hands.  Having the best cards or players is no guarantee of success in either game.  The skill/luck quotient feels very similar, with our skill constantly being tested against our opponents’ luck.  Both games involve numerous risk/reward decisions.  In neither game will perfect decisions always be rewarded.

Mucking Aces Alexis AgueroOn the contrary, they are often punished.  Like when we transfer out players who are in poor form and about to embark on difficult fixture runs.  Trading such players out for ones in good form facing easy fixtures seems like a no-brainer, but time and again, discarded players make a mockery out of the form book.

It’s like making disciplined folds in a poker tournament with a marginal hand like Ace10 off-suit in early position on a high action table, only to suffer the torment of a King, Queen, Jack rainbow flop, meaning we’d have had the best hand possible (AKQJ10 for the nut straight) had we not folded.  And then we die a little more inside as two players go all-in and fail to overtake our nut straight, meaning we could have trebled our chip stack.

Does this mean we were wrong to transfer/fold those players/hands?  Of course not.  It’s simply ‘standard deviation‘.  Take flipping a coin 100 times.  On average you’d expect there to be 50 heads, but because of ‘standard deviation‘, around 32% of the time there will be fewer than 45 heads or more than 55.  In other words, swings (up and down) are inevitable.  The right way to deal with downswings is to simply accept the fact that they happen, remain calm and keep playing your best game.


An understanding of probability is a key component of both games.   More often than not a player considered most likely to score a goal in a match is best priced by bookmakers at odds against to do so, meaning they deem it more likely he won’t score than he will.  This weekend, for example, Jesus and Kane are currently both only even money (50/50) to score against newly promoted sides Brighton and Newcatsle

Last year’s top points scorer in FPL, failed to score in 21 of the 38 games he played in, which is roughly 55% of the time.  What’s more, Sanchez only provided an assist from those 21 games on 6 occasions, meaning in 15 games (39.5%) he scored no attacking points whatsoever.

Let us assume, however, that we ‘know‘ Sanchez will score in half of the games he plays over the next 3 seasons.  It would be well within the normal distribution of goals predicted by ‘standard deviation‘ for him to go 8 games in a row without scoring.  Especially if those are the gameweeks that I own him!  Actually, a sequence of 8 blanks in a row over the course of 100 games can be expected to occur around 17% of the time.  Likewise for 8 scoring games in a row, which naturally will happen when I don’t own him!

SD socks

‘Tilt’ is a poker term for a state of mental or emotional confusion or frustration in which a player adopts a less than optimal strategy, usually resulting in the player becoming over-aggressive – Wikipedia.

Going on tilt is something that FPL players are also particularly prone to, commonly in the form of making ‘rage transfers’ for multiple hits, especially after low scoring gameweeks.  Playing hundreds of thousands of hands, however, has reconciled me to the harsh reality of ‘downswings’ being an inevitable part of poker.  Poker has taught me to be a lot more accepting of bad luck, which in turn has helped me to handle the bad beats routinely administered by FPL with more serenity than most managers are seemingly able to muster.


Planning ahead is key to being a good poker player.  There are 3 streets of betting after the pre-flop action.  Namely, flop, turn and river.  In order to run an effective ‘triple barrel bluff’, for example, we need to size our bets on the first 2 streets (flop and turn) in such a way so as to be able to make a big enough bet on the third (river) to discourage opponents from calling.  The mistakes novice players make often arise from not thinking ahead.  As a result, they’ll fire pot-sized semi-bluffs on the flop and turn say, only to discover if they miss their draws that they only have enough chips left to bet a small fraction of the pot on the river.  This makes ‘crying calls‘ or ‘hero calls‘ of their bluffs so much more likely.  When I act on the flop, I do so with a clear plan for how I intend to play the next 2 streets (turn and river) also.

Carrying this mindset over to FPL means I’m always thinking two or three gameweeks ahead at the very least when making transfers.  In this way, self-inflicted predicaments can be avoided.  A good example from last season was my keeping a Spurs slot free in my squad (as well as sufficient funds in the bank*), for when Harry Kane returned from his second injury spell.  That was at a time when lots of managers already had 3 Tottenham assets in their team (predominantly drawn from Walker,

Davies, Alli, Eriksen and Son), which meant a minimum of 2 transfers would be required for them to acquire Kane.
[*NB:  Good ‘bankroll management‘ is essential in both games: in FPL to ensure funds are available for our marquee signings; and, in poker to avoid going bankrupt!]

FPL managers are generally reluctant to take points hits for so-called ‘sideways moves‘ no matter how preferable an alternative player from the same team might be, and my late charge up the rankings towards the end of last season probably owed much to the significant number who stuck with Alli and Eriksen, rather than twist to Kane.  Such an aversion to sideways moves was to prove especially costly, as Kane scored a massive 71 points in the last 7 games, averaging just over 10 points per match (PPM), on his way to winning a second consecutive golden boot.

EV people

Most of the decisions I make when playing poker are informed by the concept of ‘expected value‘ (EV).  Essentially, I’m always asking whether or not my next actions are ‘+EV’ or ‘-EV’, and if my lines are the ones that extract the most value on average.  My aim, therefore, is to find the sweet spot with bet sizing and actions that make me indifferent to what my opponents do next, because in the long run these lines of play will show a profit.

In the Pot-Limit Omaha cash games I play, it’s not unusual to be heads-up facing an all-in pot-sized bet on the flop, when I am 50/50 to win the hand.  Effectively, each player is getting 2/1 odds on an even money shot, so it’s a +EV scenario for both.  Setting aside what has already been invested into the pot hitherto, the EV of folding to the all-in shove is zero, whereas the EV of calling is half the pot-sized bet, because the amount I win is twice as much as that I lose:

0.5*2 + 0.5*-1 = 0.5

So if facing a $60 bet, a call would on average yield $30 profit.  Naturally, this is only in the aggregate.  It’s not actually possible for me to win half the pot-sized bet on an individual call, as I can only either win 2 bets or lose 1.  Calling is clearly better than folding.  Somewhat perversely perhaps, calling in the example above is still correct even if we ‘know’ we only have a 40% chance of winning and are more likely to lose than win:

0.4*2 + 0.6*-1 = 0.2

No matter how many of these ‘coin flips‘ are lost to begin with, calling will always come out ahead in the long run.  Our friend ‘standard deviation‘, however, ensures that the long run can be much longer than most people think.

Maximising expected value is at the core of winning at poker.  And so many decisions in FPL can be thought of as EV ones too, where we weigh up the relative pros and cons of our next moves.  We are continually faced with ‘coin flip‘ situations in FPL.  Viewing transfer options through the prism of EV leads to more optimal play in my opinion.

By way of example, I remember an EV decision I made with my second FPL wildcard last season, concerning which two attacking assets from Spurs to own.  Namely, whether to go (for reasons of budgetary constraints) with Kane and Son, or Alli and Eriksen, both of whom were in a rich vein of form, and a popular double up at that time.  My calculation that the expected value of the former was greater than the latter proved to be vindicated in no uncertain terms, and propelled me up the overall rankings to good effect.

EV Dilbert
Obviously, the variables in FPL are infinitely harder to quantify than is the case with the precise probabilities that apply to poker.  The best we can do is analyse available historical data, and consider metrics like points per match (PPM), or minutes per point (MPP).  Furthermore, we can cross reference these findings with big chances (BC) and shots on target (SOT), which I deem to be the reported statistics* with the strongest correlation to future goals, and FPL points.

[*NB:  xG or Expected Goals have now begun to be reported more widely this season, and are even better indicators in my opinion.]

In the case of Kane & Son vs Alli & Eriksen, I don’t have any record of my estimates at that time, but for the sake of argument, let’s say my expectation was that Kane would average 9 PPM, Alli 7, Eriksen 6 and Son 5.  Such a process would lead me to conclude Kane and Son were the optimal pairing as (9+5) > (7+6).
With hindsight we know Kane and Son averaged 10.1 and 5.7 PPM respectively, for a combined 15.8 PPM, whilst Alli and Eriksen averaged 5.3 and 6.1, for just 11.4.  My EV decision averaged 4.4 PPM more than the alternative over the last 7 games, netting me an extra 31 points overall.

Kane and Son

Unquestionably though, 10 PPM is not sustainable by any player, but I’d argue that the lower output elsewhere merely serves to highlight that ‘regression to the mean‘ is just as much a feature of FPL, as it is in poker, and most other places besides.
Now you might feel this is a case of me finding facts to fit my theory.  After all, this is the same Harry Kane who incurred the wrath of managers earlier in the season by scoring appearance points only in consecutive home matches against Burnley and Hull.  Alas, disproving allegations of planting and rigging the evidence, doesn’t fall within the remit of this article, and I’m out of time.

In the meantime, may the FPL flops be with you.

Coley a.k.a @barCOLEYna3J5 flop

This is your very first post. Click the Edit link to modify or delete it, or start a new post. If you like, use this post to tell readers why you started this blog and what you plan to do with it.