Predicting Individual Player Contributions – The Next Frontier

I’ve recently been inspired by @DoctorFootie’s really creative model of predicting possession – I won’t pretend to understand it well enough to explain it, but the idea is that he borrowed an equilibrium model from chemistry to predict how much possession each team will have in a given game. The significant contribution here is that opposing team stats contribute to and affect each other, and this is most obvious in a place like possession where you have a zero-sum game (me having the ball means you can’t have the ball). It would be interesting to see how well this works for something a little less zero-sum (number of passes) or almost completely independent (tackles, or maybe even shots on goal). That’s not my method, but you all should follow @DoctorFootie on Twitter because I’m anticipating he’s going to do some interesting things going forward.

Back to my inspiration – my current model uses average player stats to predict results (on top of a “strength” coefficient calculated from the 2014-2015 season). 1 But I can do better than this – I can predict individual player stats based on who they are facing. It’s easy enough to do – player stats would be your dependent variable, aggregate opposing team stats would be your independent variable, then let the machine learning models work their magic on this training data.

Then I could do predictions based on average opposing team stats – how many passes will a player make given the quality of opposition? How many shots on target will a player get?

This could potentially give better predictions for player stats to put into the current model, but there’s a bigger issue here. This would be another step closer to quantifying individual player performance – knowing how individual¬†players perform against each opponent could be incredibly useful for teams: it could help managers trying to make the optimal squad selection, could help teams decide which players to purchase, and could get use closer to understanding exactly what individual players add to the team contribution and under what circumstances do they add it. Are there “big game players?” How consistent are players? Can a player move up to compete against stronger competition?

This will be my holiday goal – I think I can put something preliminary together relatively quickly after my grading is done, test it, see if it works and is worth pursuing more fully. I might test two or three teams to see how it works, and go from there. Hopefully we can learn something interesting here and create an improved model of individual player contribution over what I have now.

  1. As I type this, I’m realizing I can weight the strength coefficient from season to season – e.g. week 1 I only use the 2014-2015 coefficient,¬†maybe by week 10 I can start to slowly add in the 2015-2016 coefficient

One thought on “Predicting Individual Player Contributions – The Next Frontier”

  1. Interesting concept, when I have looked at how player stats change according to the strength of opposition (based on bookies odds) I have seen some counterintuitive increases and decreases.

    So I think you are on the right track, the big challenge will be to have enough of a data set, in order to set realistic benchmarks for players adhering to different tactical instructions/systems.

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