Since starting up this site a couple months ago I have engaged with quite a few people with an interest in the growing field of stats and football. I'd like to introduce the first in a series of weekly articles by a very welcome guest writer on this site, SuperGrover. SuperGrover has a couple of decades of involvement with the Sabremetrics movement in baseball and NFL so knows his stuff.
On our fledgling forum 'FPL Analaytical' we have been predicting the scores ahead of the gameweek for the last few weeks. As he will explain below, SuperGrover has built a model to do this. Please note that the data and articles normally on this site are derived from my own model but I am working very closely with SuperGrover and others to incorporate the best elements from each others work to give you the best fantasy football forecasts.
Please add your views or any constructive criticism in the comments section, or check out the forum.
Gameweek 11 Predicted Scores [by SuperGrover]
Football statistical modelling is in its infancy. While no doubt major clubs have squads of
statisticians with proprietary algorithms defining player and team value, the
general population has been left to look at goal records and the league table
to determine the quality of player and team alike. That all changed with OptaStats. Now, everyone can see the story behind the
game. We can look at the activities
throughout the pitch and begin to ascertain which of these lead to goals. Further, we can begin to see which activities
indicate innate ability and which are simply the luck of the draw. By combining these we can come up with
forecasting models for both team and player, a Holy Grail for fantasy football managers.
While @shots_on_target has primarily focused on player
evaluation, I have spent numerous hours over the past months hypothesizing on
team value. I have worked to understand
the underlying activities that drive goals scored and allowed, allowing me to
construct team value models that I believe are far superior to the league
tables. While still a work in progress,
I am confident enough in these models to share them with you.
It has been known for some time that shots, more specifically
shots on target, are great predictors of goals. Teams tend to score on about a third of shots
on target on average. More importantly,
teams that exceed or fail to achieve that rate one year tend to regress towards
the mean the following (see James
Grayson’s excellent blog for more discussion). As a result, we can use shots on target
rather than goal scored as a better indicator of team performance, mainly due
to the sample size issues in goals scored (logically, there are about 3 shots
on target per goal scored). This helps
us identify teams that maybe underrated or overrated based upon goals alone,
especially early in the season when sample sizes are low.
But are shots on target enough? It certainly is a start and much better than
plain old shots or goals scored as a forecaster. Yet, to me it seemed…wanting. (ed: I agree, more is needed! SoT) I looked for some other factor that may do a
better job of explaining things.
What I have found is that shots on target in
combination with “Big Chances” (BC) result in a stronger correlation
than shots on target alone. BCs are
defined by OptaStats as follows:
Big Chance A
situation where a player should reasonably be expected to score usually in a
one-on-one scenario or from very close range.
In my mind, BCs represent shots on target on steroids. Adding BCs as additional factor results in
the following improvements in goals scored projections over the past three
seasons:
2010 – 1.75%
2011 – 2.93%
2012 – 1.02%
While the improvement is not substantial, it is consistent
enough for me to have some confidence in the model. For goals allowed, the improvement is even starker,
although the data set available currently only goes back a single season:
2011 – 7.52%
2012 – 25.37%
I have theories as to why the improvement is much greater
for goals allowed, but I will save that for another time.
Beginning next week, I will post my team ratings and goal
forecasts using this model with commentary on weekly performances. For now, I will post the projected scoreboard
for this week’s games. I encourage you
to check out our gameweek
predictions forum for additional projections from other members.
So there you have it, the nuts and bolts of my current
model. I am still tweaking things and
looking for further improvements, but I do feel it is a good start. Comments and criticisms are very welcome.
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