As promised, it's time to take a step back from forecasting and projecting and look at how the model has performed since I rolled it out in Gameweek 8. There are three ways to look at this:

- Look at each player's weekly forecast score versus their actual weekly score
- Look at each player's aggregated forecast score versus their actual aggregated score
- Look at each player's average forecast score (per 90 minutes) versus their average actual score

Each measure has it's advantages and disadvantages but I will throw the first option out as any model is simply not going to be accurate enough on a weekly basis enjoy great success. Of course, we can use its outputs to forecast the

I'm not going to run through individual players but if you would like to discuss a player in more detail, feel me to do so in the comments. A few high level points:

*probability*of different players' chance to succeed but even if it was perfect we'd still see massive fluctuations. So that leaves options two and three which each have some advantages: option two is the truest comparator of how the model performed over the period while option three does a better job at accounting for the fact that players don't play every week. As a compromise, I am comparing points on a total basis but I've thrown out all games where players played 45 minutes or less as they tend to mess up the correlation stats and aren't really representative of what the model is trying to achieve. Trying to forecast decisions by the likes of Mancini need a more powerful computer than a MacBook.- The model seems to be performing quite well, and if we limit ourselves to players who have racked up 450 or more minutes, we see a 70% correlation and 0.5 r-squared which are reasonable (though hardly indicative of a perfect model)
- The fact that the line intersects the y-axis at ~three points suggests that the xP measure is probably underestimating scores across the board. This is reasonable for a couple of reasons. One, the model is based on average outcomes and thus will never forecast the kind of 23 point performance we saw from Cazorla this week. Second, I currently crudely account for bonus points by simply awarding 1.5 points per goal (the average points earned for a goalscorer). In reality of course, a goalscorer will often earn all three points for a scoring effort which can impact scores here, particularly at the top end of the market.

The main trend most people will observe is that the 'elite' players are above the line and thus the model is tending to undervalue them. In some ways, mainly due to the aforementioned issues in point two above, this is true and I'd be happy to concede that some more work is needed to better apportion bonus points (which will overwhelmingly flow to elite players) and perhaps reduce the regression rates if we can establish that the elite players really can exceed league average in terms of goals per shot on target with any true consistency.

That said, we need to be careful not to get caught up in confirmation bias by anointing those players with high scores as 'elite' (are we doing so because of their talent of because of their recent points hauls?). Rooney and van Persie are obviously some of the league's best players but it's fair to say that Fellani and Michu wouldn't have been in that conversation at the start of the season, while many were getting frustrated with Cazorla until his huge haul this week. While there's no doubting there could be something here, other 'elite' players like Suarez, Ba and Hazard have essentially performed in line with the model, with Aguero significantly below, so it's overly simplistic to simply state that the model is too harsh at the top end of the market.

In terms of using this information, this graph should not necessarily be used as a indication of future regression. For example, if a player steadily increased his shot production over this period, the model would always be behind in terms of forecasting his production and by the end of the period his points would reasonably (and sustainably) outshine his xP. It

*can*however be used to spark those conversations though, and to help us look into players who might regress, but it's not as simple as everyone regressing towards that trend line.
Overall, I'm pleased with these results and they form a good starting point for future developments. If we look at the distribution of variances we see that they form somewhat of a familiar bell curve, without too many huge variances at either end of the spectrum. 79 of the 104 players in the sample fell within 10 points of the expected points, which isn't too bad over a 10 week period.

The next step is look at tweaking the formulas to try and understand those variances at the top end of the chart and determine how much of the difference is attributable to their true skill exceeding the average and how much is attributable to confirmation bias and revisionism. As always, any suggestions are welcome, and I'll do another update like this in a few weeks to report back on any updates or developments.

## 6 comments:

Could you display the first graph displaying only the top 10 Midfielders and Forwards...

And one more request, Chris.

Can you do this graph without the addition of the bonus points...

Thank you, and as usual a brilliant post.

I can't state enough how helpful you blog has been to me. Keep up the good work.

My time as a poker player has taught me that eventually the creame usually rises to the top given a large enough sample but it also taught how how some players can run above expectation for extreamly long periods of time.

I've run varience sims alot and the deviation even within large samples can be to scary to look at. I bring this up cause I believe that it plays a massive role in players performances and is extreamly difficult to distinguish between skill and positive varience when looking at the truely Elite players.

Thats not to say we shouldn't try:)

Odds are that Michu and Fellaini are running way about expectation and are not truely elite but sometimes you just have to hop on the +EV train and ride it till the end(look at Cisse in the 2nd half of last season).

I might have come off a little black and white in my post. We know there are so many intangable in football that we can see with our eyes but couldn't describe well with numbers. As you mentioned the types of SOT/chances RVP will have at the head of Uniteds attack will be of a higher quality than say Long for WBA but how can we quantifi that with numbers?

Long time reader, first time commenter.

First of all (and more importantly than anything else), your analysis is hands down the best in the business! Keep up the good work.

Regarding the performance of the model - I think it might be informative to compare it to a naive benchmark model to see how much better your model does in explaining player performance. One naive model that comes to mind is simply using the average points from the first 7 gameweeks to predict GW8-17. The extent to which your model does better than such a naive forecast is the best measure of its success.

I don't have much constructive to say, I'd just like to thank you for another great post. The work your doing is fascinating and helpful! Keep it up. Cheers.

Henry - I've added a new chart which can be accessed from the menu bar, which allow you to filter by team, position and/or price. As or bonus points, they're currently baked into the model calculation, but it's a good idea to maybe look at things with them excluded so I will work on that over the holidays. Thanks for the suggestion!

CDI - thanks, your kind words are very appreciated. Good points. I'm still learning about regression and statistical variance etc and I'm always amazed by how much variance we would expect just because random chance. I'd like to try and understand if, even if someone like Michu isn't "elite", is there anything that can be found to show he genuinely is playing like an elite player, at least for the being (rather than just getting lucky). For example, his goal against Arsenal where he was through one-on-one I really felt he would score, and while that's likely just confirmation bias, there must be something in it, that if he feels the same way it could have a genuine impact on his performance. My limited statistical knowledge suggests otherwise but I wouldn't dismiss it out of hand. Interest field to look into.

I definately think there's work to be done on the quality of on-target shots. I truly believe that getting on the end of a cross is a skill, but once you do it's a lot of luck and reliance on the opponent keeper that plays a big role in whether you score or not. They should be distinguished from chances like Aguero's this week, where City's class essentially gave him an almost certain goal.

Agnar - excellent suggestion. I will try and post those numbers this weekend. My hunch is that it will be marginally better but probably not a great deal different for the majority of players. I hope that the strength of the model is identifying outliers like Steve Fletcher who earned 9.75 PPG through four weeks and received 200,000+ transfers but was obviously overachieving to a crazy level (3.5 PPG since).

Unknown - thanks for the kind words

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