A Visual Look At Shots On Target

This is part of my efforts to try to come up with a zone rating of sorts for goal keepers. The problem I’m running into at the moment is trying to find visual information for shots against. If I want to know how good Dan Kennedy was preventing goals against Columbus in week 1, I have to go to Columbus’ page on Squawka and narrow the shot data to that specific game. Basically, It just boils down to more time digging than I initially planned to devote.

Quickly, here is a visual graphic that I made with the help of Excel. I know it’s not really pretty, but it delivers the data in the manner in which I needed it without getting caught up on eccentric details, details with which I often spend too much time meddling.
Shots+Goals and visuals

There isn’t a lot that this immediately tells you, of course. It’s more of a jumping off point to start comparing data once it is collected. That’s where the next effort is going to be headed. Who are the teams that are above league average and below league average? Are they bleeding low percentage goals, or are they being beat in an unusual zone? This information, while still miles from being complete, moves us in the right direction of knowing more about shots and goals than what we did previously.

You’ll notice that I also included shots that are wide but still close to the post. I’m curious as to whether these shots numbers become inflated when playing teams with “better keepers”. Unfortunately we need to define what better is. Better than what, exactly? I’m not sure. Again, parameters haven’t been set, and data sets are still being gathered.

This is a fun exercise and one that should, if nothing else, provide us with some excellent insight to teams and their seasons at this point.

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ASA Podcast XVIII: The One Where We Discuss Defense Influencing Shots

Family in town, waiting out this baby and being home doing nothing sure has made me lazy. So lazy that I really didn’t get around to editing this and putting it together until last night. My apologies for the late posting.

This week we discuss the USMNT and their romp in eastern Europe, a bit about Montreal and Omar Gonzalez. Then we transition to some discussion about whether goal keepers can influence shots on target. It’s all some interesting stuff with a lot of giggling by me because I coined a new nickname for Drew.

 

Noisy Finishing Rates

As a supplement to the stabilization analysis I did last week, I wanted to add the self-predictive powers of finishing rates—basically soccer’s shooting percentage. Team finishing rates can be found both on our MLS Tables and in our Shot Locations analysis, so it would be nice to know if we can trust them.

Last week I split the 2012 and 2013 seasons in half and assessed the simple linear relationships for various statistics between the two halves of each season across all 19 teams. Now I have 2011 data, and we can have even more fun. I included bivariate data from both 2011 and 2012 together, leaving out 2013 since it is not over yet. It is important to note that I am not looking across seasons, only within seasons. To the results!

Stat Correlation Pvalue
Points

0.438

0.7%

Total Attempts

0.397

1.5%

Blocked Shots

0.372

2.3%

Shots on Goal

0.297

7.4%

Goals

0.261

11.9%

Shots off Goal

0.144

39.5%

Finishing

0.109

52.1%

Surprisingly, to me at least, a team’s points earned has been the most stable statistic in MLS (by my linear definition of stability). Not so surprising to me was that total attempts is also one of the most stable. Look down at the very bottom, and you’ll find finishing rates. Check out the graph below:

 Finishing Rates Stabilization 2011-2012

Some teams finish really well early in the season, then flop. Others finish poorly, then turn it on. But there’s no obvious to pattern that would allow us to predict second-half finishing rates. In fact, the best prediction for any given team would be to suggest that they will regress to league average, which is exactly what our Luck Table does. It regresses all teams’ finishing rates in each zone back to league averages, then calculates an expected goal differential.

On a side note, you might be asking yourself why I don’t just use points to predict points. Because this: while the correlation between first-half and second-half points is about 0.438, the correlation between first-half attempts ratios and second-half points is slightly stronger at 0.480. Also, in a multiple regression model where I let both first-half attempts ratio and first-half points duke it out, first-half attempts ratio edges out points for winner of the predictor trophy.

Estimate Std. Error T-stat P-value
Intercept 1.7019 5.97 0.285 77.7%
AttRatio 13.7067 6.32 2.17 3.7%
Points 0.3262 0.19 1.691 10.0%

And since this is a post about finishing rates…

Estimate Std. Error T-stat P-value
Intercept -2.243 7.75 -0.29 77.4%
AttRatio 18.570 5.71 3.26 0.3%
Finishing% 63.743 50.08 1.27 21.2%

A good prediction model (on which we are working) will include more than just a team’s attempts ratio, but for now, it is king of the team statistics.