Juni Bundesliga. Im tiefen FCSP-Twitter-Kosmos Ob sich Ähnliches auch aus den expected Goals schließen lässt? Hier jedenfalls die Tabelle für. Visit ESPN to view German Bundesliga statistics, along with a season-by-season archive. Dez. Immer häufiger werden dabei die "Expected Goals" aufgeführt. In den Statistiken, die nach Bundesligaspielen auch öffentlich zugänglich sind.
Theoretically, SoTR could be a nice method to lose the noise that weakens TSR in later stages of the season, hopefully without losing too much of the early signal that makes the method so powerful.
I was wrong, it seems. Despite holding roughly one third of the sample of TSR — around 1 in 3 shots is on target — the SoTR metric picks up its signal equally fast and holds it longer.
Just like it theoretically should! At its peak of predictivity, the mid-season, SoTR performs notably better than TSR, which should make it the preferred method to treat raw shot counts.
As said before, not all shots are equal, and the capacity to get shots on target seems to hold predictive power for future performance. Partly this may be the effect of better teams simply firing more accurately, but it may also contain information about playing in favourable game states.
Next up in football analytics land was the appearance in of Expected Goals models. Simply said, each shot is assigned a number between 0 and 1 to reflect the odds of such a shot resulting in a goal.
This process is not done subjectively by hand, by objectively, by using large databases of earlier shots and determining correct odds by regression methods.
Expected Goals models do differ a slight bit from one model to another, but the mainstay of the input is shot location and shot type. The conclusion from these graphs is quite simple actually.
Expected Goals Ratio forms an impressive improvement on raw shot metrics at each and every point in the season. It picks up information much like the raw shot metrics do in the very early stages, then predicts future performance significantly better at early to mid-season, and also holds predictive capacities for longer.
It makes sense to use Expected Goals Ratio from as early as four matches played. Even that early, it is as good a predictor for future performance as Points per Game and Goals Ratio will ever be.
This is very nice work Tegen but surely you cannot plot the Expected goals ratio for a whole league and expect it to be an accurate predictor for every club in the league?
I mean the correlation for the majority of teams might be excellent but a few outliers above and below the correlation line will keep everything looking hunky dory when in fact the individual teams in league itself may vary quite a bit from the correlation?
I see you say you can fit the correlation from as early as game week four but as we all know the variability in fixture strength and form for teams in the early season can lead to wildly erratic differences in points per game or goals per game or shots per game compared to say the correlation you will get after 12 or 15 games when we have more data to go on.
Have you looked at the difference between the correlation for the top 3 of each league compared to the bottom 3 for example? All points in these plots are an R-squared value.
Those values are all derived from regressions in scatter plots. Each scatter plot holds two points of data per team: So each scatter plot has as many dots as there are teams in the dataset at that match day.
For match day 1 this is all teams from all eleven leagues tested, up to match day Beyond that, teams from the Bundesliga and the Eredivisie are not in the set anymore, so the plots from match day 34 to 37 are done on teams from the remaining 8 leagues.
Obviously, the predictive power of all metrics increased as they are fed more information during the early days of the season.
This holds true for all metrics alike though. The reason the graphs work so well could just be that their are an equal number of quality teams getting ultra consistent results which balance out the poorer teams which get inconsistent results and likewise form teams and teams out of form?
Forgive me for not completely understanding your point still. How could this be of a different influence to different metrics? All that R squared does is compute the distance between the points on a scatter plot and the regression line.
Maybe it is for the average team in the league on average form against average opposition but by not differentiating between the good and bad sides and excluding the form element etc I think the model cannot possibly be effective.
My bold statement is that form only exists after an event. I have yet to see any evidence of people being able to show evidence of teams in or out of form prior to an event.
On your second point. An ExpG based team rating does an excellent job of separating good and bad sides, as is reflected by the correlations with future performance indicators.
All sorts of teams, be it good, mediocre or bad, are in this dataset, so the correlation with future performance reflects all kinds of teams.
As I say but maybe not clearly enough I think this model would be fine for predicting goals or points returns for the average team in average form playing a fixture of average difficulty.
I loved your previous ExpG work for individual teams and while the limitations listed above are still relevant to these individual teams availability of playing resources is another variable I think it is somewhat easier to interrogate the data and the reasons for outliers and to distinguish between candidates for regression or teams playing to a sustainable level.
I am only new to predictive modeling and am learning mainly through your fine work so I am not aware of any potentially better models out there.
I was just trying to offer constructive feedback on potential limitations with your model which might help you to improve it in the future.
No worries, this type of debate should be conducted to improve understanding of the model, and to improve the model itself. For predicting future performances of individual teams one should simulate each remaining match for that particular team.
This process involves Poisson distributions around expected match scores, repeated many times to get correct percentages for wins, draws and losses.
It clears things up somewhat but if you are using average correlation figures for specific teams I still think the model cannot be very effective.
Have you checked the efficacy of the model by predicting say 5 random teams goals and points over the next 5 or 10 games?
Why are you considering ratios instead of differences? You use these stats in your prediction models as well, but imagine a very defensive team that creates 0.
This team has an excellent ExpG ratio, but will obviously not be able to win a lot of games and will only end up in the middle of the table due to a lot of draws.
This example is a bit extreme, but I can imagine this kind of thing creating a small bias, which would not appear using ExpG difference instead of ratio.
I think your model could benefit from the following. Are teams always playing at their best? Both expected an easy win, but acted differently. Ajax will build up more ExpG and your models will predict them to be better, but are they really?
I think this is one of the reasons Feyenoord is a bit overrated in your models: By the way, it might be that my examples are not correct and I am interpreting the match outcomes wrong, but correct or not, this illustrates what I want to say.
This problem could be avoided by looking at the correlation between ExpG and strength of the opponent. If there is a strong correlation like Feyenoord and Ajax in the example , a team is probably not able to perform in matches against strong teams, while a weaker correlation like PSV probably means they are not trying hard against weaker teams and are hence underperforming a little, and the model is underrating the team.
Regarding your first point, the use of ratios over differences. However, I should probably repeat that test now, given that fact that we have more data available, and the testing was done prior to constructing my ExpG model.
Your second point concerns teams tactical choices, manifesting themselves in more or less effort in matches against particular opposition.
This is an interesting potential source of bias indeed. They have in fact been one of the worst defensive sides in the league, conceding an average of 1.
Atalanta are one of the form teams in Serie A at the moment, having won their last four matches, scoring 14 goals in the process. Their last game against Inter Milan showcased all that was good about Atalanta, as they ran out deserved winners following a great display.
Freiburg vs Werder Bremen Sunday, Freiburg come into this game on the back of one win in six matches after losing to Mainz las time out, though they were extremely unfortunate to lose that game according to expected goals xG: In their six home games so far, they have generated 8.
Werder Bremen sit seventh after a steady start, but have deservedly lost their last three matches, conceding a host of chances in all 7.
Werder have been second best in all of their last six matches according to expected goals, and could be on the end of another defeat here. OK, I get it.
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