EPL: Cracking The Code On Premiership Fantasy

Author Devin Pleuler is miles ahead of you when it comes to fantasy “football” preparation…

Wenger's, Le Professeur...but he can't match this.....

It may come as no surprise that in the Fantasy Football competition hosted on the English Premier League’s website, the more a player is worth doesn’t necessarily mean that that player will produce more fantasy points. There are many reasons for this — some obvious, and some not so obvious. Some of the more obvious reasons are things such as injury, suspension and particular players falling out of favor or being constantly rotated (to name a few). While this article provides some small gains by applying some mathematics to this complex system, these gains can be negated by falling behind on the latest club news or even your fellow participant’s sheer luck. While I believe that the transfers that I have made to my fantasy team are very close to optimal, I am guilty of dropping obvious points for these exact reasons.

With the recent release of the blockbuster Moneyball, based on one of my favorite books of all time, it is fitting that we are taking an intimate look at the game’s statistics and trying to determine what stats are good ones, and which ones are misleading.

Perhaps it makes sense to set a goal for this article, since I do not claim to have the mathematical formula that is the holy grail in terms of fantasy football. Instead, we shall aim to create a statistical model that correlates to a player’s total fantasy points much more closely than the player’s fantasy value.

Lastname Team Fantasy Value Fantasy Points Fantasy Points Per Value
Lampard Chelsea 13.0 130 10.0
Fabregas Arsenal 11.7 128 10.9
Gerrard* Liverpool 11.3 88 7.8
Malouda Chelsea 9.7 186 19.2
Kuyt Liverpool 9.7 117 12.1
Van der Vaart Tottenham 8.9 165 18.5
Cahill Everton 8.9 117 13.1
Arshavin Arsenal 8.7 138 15.9
Silva Manchester City 8.6 144 13.3
Young Aston Villa 8.4 160 19.0
Adam Blackpool 6.0 192 32.0

*Gerrard only played in 20 EPL games.

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As you can see here, just by listing the top 10 valued midfielders from last year’s competition, you see a very wide array of efficiencies, with Florent Malouda leading the top ten with about 19.2 points per fantasy value unit. However, if we proceed way down to Charlie Adam and his 32 points per fantasy unit value, you begin to realize just how inefficient this player market is.

So, lets do our first regression analysis with fantasy points as the dependant variable and fantasy value as the sole independent variable.

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The regression creates a line with an y(Points)-intercept of -26.25 with a slope of 1.65 points per unit value. However, the fit line is not very strong, with R-Squared value around 28%. This is the figure that we are going to look to improve upon by finding better statistics to predict a players expected fantasy point production.

As noted earlier with the asterisk next to Gerrard’s name, he only participated in 20 EPL games during the 2010/2011 season. Therefore, with only 88 fantasy points at the price of 11.3, his efficiency took a tremendous hit. However, it is very easy to include minutes played as another independent variable in the regression to compensate for this.

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Danny Murphy...wrong side of this analysis buddy...

As shown by this graph, minutes played correlates much more closely to total fantasy points than player value, with an R-Squared value of around 80% (already considerably better than value alone). Interestingly enough, no midfielder that played over 3,000 minutes during the 2010/2011 campaign scored less than 106 points (Danny Murphy of Fulham).

When we use both value and minutes as independent variables (and points again as the dependant variable) in a regression (which I will not graph due to the extra dimensionality), we get an R-Squared value in excess of 87% – a significant gain. However, this is misleading because minutes and value probably are not truly independent variables. Value, in this game, is heavily related to popularity. Since a player that plays more minutes is probably more likely to be popular (and 1 to 2 points are awarded to each player for merely participating in the game), it is clear that these variables are dependant to a certain degree. We need to search for variables related to a player that are more independent, and this is a very hard task.

So again thinking in terms of midfielders, we need to come up with some statistics that we feel better represents a midfielder’s ability to create fantasy points. Some obvious stats that we can test are shots taken, passes completed, passes received and pass completion ratio. And, not shown in the table below (and slightly less straight-forward), passes received in the final third, average position when receiving a pass and average position when passing.

Lastname Shots Passes Completed Passes Received Completion Ratio Fantasy Points
Lampard 54 1086 1032 .79 130
Fabregas 48 1303 1317 .78 128
Gerrard 36 1050 945 .74 88
Malouda 71 1496 1457 .76 186
Kuyt 48 1056 1047 .70 177
Van der Vaart 81 1079 985 .77 165
Cahill 41 620 560 .73 117
Arshavin 57 819 890 .69 138
Silva 39 1378 1240 .83 144
Young 73 902 885 .64 160
Adam 79 1378 1182 .67 192

———-

This regression analysis on the data set gets a considerably better R-value of 91%. The formula of the regression line represents the expected amount of fantasy points given a player’s individual statistics.

Lastname Team Fantasy Value Fantasy Points Expected Pts
Lampard Chelsea 13.0 130 110
Fabregas Arsenal 11.7 128 130
Gerrard* Liverpool 11.3 88 109
Malouda Chelsea 9.7 186 190
Kuyt Liverpool 9.7 117 136
Van der Vaart Tottenham 8.9 165 154
Cahill Everton 8.9 117 96
Arshavin Arsenal 8.7 138 131
Silva Manchester City 8.6 144 142
Young Aston Villa 8.4 160 162
Adam Blackpool 6.0 192 156

———-

It’s clearly an imperfect model (as suggested by both the imperfect R-squared value and the dependence of some of the variables), but it certainly provides a much better guideline for judging a player’s fantasy point production than the players value alone. (Interestingly, if we add the player’s value to this regression, we see less than a 1% improvement on the R-squared value.)

Now, let’s apply this same model to this year’s (2011/2011) data through Gameweek 11 and try to figure out which players are expected to produce the most fantasy points for their respective value.

Lastname Team Fantasy Value Fantasy Points Expected Pts Points Per Value Unit
O’Hara Wolves 5.7 41 64.6 11.3
Moses Wigan 4.7 23 48.2 10.3
Johnson Norwich 4.6 37 40.3 8.7
Faurlin QPR 5.0 32 43.5 8.7
Petrov Bolton 5.7 37 46.9 8.2

———-

However, in gameweek 12, none of these players scored more than 2 points; Petrov only played 22 minutes and O’Hara accumulated the threshold number of yellow cards for a suspension. The search for the most efficient players seems to be much more of an exercise in finding statistical anomalies. But, looking at the amount of actual fantasy points that these particular players have created, gameweek 12 seems to have been a statistical anomaly in itself. Myself, carrying three of these players, still managed to score significantly higher than the league-wide average.

For completeness’ sake, here is the top 5 expected points producers through the first 11 gameweeks.

Lastname Team Fantasy Value Fantasy Points Expected Pts. Ex. Pts. / Value Unit
Silva Man City 10.3 74 66.4 6.4
O’Hara Wolves 5.7 41 64.6 11.3
Downing Liverpool 8.1 28 58.2 7.2
Bale Tottenham 7.9 53 56.2 7.1
Dempsey Fulham 8.3 53 49.2 5.9

———-

A systematic approach to finding these independent variables isn’t within the scope of this article, but machine learning and other methods can be used to find much stronger variables than the variables we hand-picked ourselves. In fantasy football, where participants benefit by luckily picking statistical anomalies and outliers, this kind of expensive variable selection likely wouldn’t yield much of an improvement over hand-picked variables. For real football analysis performed by multi-million dollar clubs and cutting-edge companies, this kind of analysis is indeed worth their time.

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14 responses to this post.

  1. Posted by Antonio H. on 2011/11/22 at 8:13 AM

    I think playing fantasy EPL is about 20 times more subjective than it is objective. Sometimes it’s about getting lucky, but you haev to look at things for yourself like a particular teams upcoming schedule, whether they’re playing home or away, how they perform at home and away, etc. Sometimes you have to stick with a certain player’s point output(Suarez, Rooney right now) for the short run but also seek out viable alternatives to hold you off until that player starts racking them up, especially when other players start to sell them.

    I look at the style of play of teams for fullbacks and midfielders and for forwards, look at who the wingers are providing them service. Perfect example, Adebayor vs Bent this year.

    A lot of it is finding the midpoint between stubbornness and being quick on the draw: how long can you survive with the current form of Ashley Young and Luis Suarez, given their fantasy reputation, before you acquire Van Persie, who’s injury prone?

    Reply

    • Posted by matthewsf on 2011/11/22 at 8:23 AM

      “I think playing fantasy EPL is about 20 times more subjective than it is objective.”

      Antonio — that’s what they said about Arsene Wenger’s team during the early 2000′s :>

      Reply

    • While 20x is a clear exaggeration, I actually agree with your general point (E.g. the first paragraph). I have used this system of analysis to pick out players that can most efficiently supplement the obvious picks like Rooney, RvP and Silva.

      And, while I didn’t list my forward picks, Adebayor and Crouch were among the top in terms of efficiency. They supplemented Captain Van Persie quite well this week with 2 goals and 2 assists between them.

      Other picks, as you suggest, are quite subjective. For example, Stoke’s Walters is listed as a Midfielder on the site – but he’s been deployed as a striker alongside Crouch. His goal this week, since he is designated as a Midfielder, awarded me 5 points instead of 4.

      I think it is fair to consider this approach a little more Stoke than Samba.

      Reply

      • Posted by Antonio H. on 2011/11/22 at 11:51 AM

        That’s the other thing I forgot to mention although I think I alluded to it: midfilders playing as SS or CAMs behind a lone striker, I.e. VdV & Walters. Although there haven’t been that many successful one’s have there? Sessegnon, Cahill(?)…?

        Reply

  2. Posted by dave p on 2011/11/22 at 8:41 AM

    Based on the analysis done, how is YOUR fantasy team doing this year?

    Reply

  3. Posted by wixson7 on 2011/11/22 at 8:46 AM

    You know I love TSG, but holy dork-o-rama……

    Reply

  4. Posted by GM on 2011/11/22 at 12:54 PM

    As someone who uses stats regularly at work and a huge footballing fanatic, I like reading this stuff. While this is good for fantasy football, how about applying to real life? I’m one of those who belive (just gut feel) that passes received / completed are huge indicator to a players ability. The better his movement and situational awareness, which are a function of ability to read the game, has high correlation (.90 or better) to passes received / completed.

    To be meaningful, you’d need to measure defenders, mid-fielders, and forwards differently. For example, positioning for a defender is not tracked, so near impossible to measure. Looking around and knowing what’s going on is critical for mid-fielders to have successful passing, but again — this is not tracked. And the number of penetratign runs and the when and where isn’t tracked for forwards.

    So in the end, we are left with only those things we measure to determine a players abilities. And scoring goals is #1 for forwards. Passing accuracy for center mids. Crosses for wings. And low number of shots on goals allowed is something defenders s/b rated on . . . not just the keeper.

    Reply

    • Yes and no on the passes issue. Some players if they are playing in a deep lying midfield role might be (due to the system) passing more horizontally than vertically. Since they aren’t tasked with linking up field their passing percentages are usually higher than if they were taking more risks.

      Reply

      • Posted by GM on 2011/11/22 at 3:01 PM

        Agree John. And there are defenders who struggle to play out of the back. They make many horizontal and back passes. Rely on keeper to boot it out our deep lying CM to play it out for them. That’s part of reason stats in soccer is so difficult.

        Reply

  5. Posted by kaya on 2011/11/23 at 10:40 AM

    I think Devin is so far ahead of me I can’t quite comprehend. I’ve never played fantasy football =/ Reading this for me would be like trying to play Rachmaninoff before learning to play chopsticks.

    Reply

    • Posted by Antonio H. on 2011/11/23 at 11:56 AM

      It’s not as complicated as this post makes it seem. It’s pretty fun too. Don’t judge a league by it’s top 2 teams

      Reply

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