End of Season Look at Schedule Adjusted Total Shots

This is a method designed by Micah McCurdy (@IneffectiveMath) for weighting schedule strength in the National Hockey League (NHL) using Total Shots (Corsi).  The method is described in full detail in the following link.  https://hockeygraphsdotcom.files.wordpress.com/2014/11/scheduleadjust.pdf

I was interested in how it would look using raw Total Shots For/Against from the Premier League.

End of season notes:

With the Premiership playing a balanced schedule (unlike North American sports leagues), I didn't know if Schedule Adjusted TSR would be that much different from raw TSR. Turns out, that in the end, Schedule Adjusting did little to alter raw TSR.

It is a weird year for in-season repeatability of raw TSR. R-squared of '1st half vs 2nd half' is way down to ~0.40 this year. A vast decline of the previous 5 season avg (~0.75).  TSR still maintained a high correlation to Goal Difference (r squared of ~.71).

Schedule Adjusted TSR did poorer in predicting itself than raw TSR. R-squared of ~0.37. While, versus Goal Difference, it slightly edges raw TSR, with a r squared of ~.72.

Given these results, I will probably not track Sched Adj Total Shots next season. Given the small value it adds to explaining goal difference I do not think it is worth the time I spend inputing the data.

I am glad I did it but I will appreciate using the extra 1 1/2 hrs per week it took to track and organize, on other things :)

Cheers,
Clarke

Here are the Schedule Adjusted TSR results (compiled using www.whoscored.com):

Season Totals (Matches 1-38)

Team	Total Shots For	Total Shots Against	TSR%	Schedule Adjusted Shots For	Schedule Adjusted Shots Against	Sched Adj TSR%	Dif
Arsenal	608	406	60.0%	600	410	59.4%	-0.6%
Aston Villa	415	481	46.3%	410	475	46.3%	-0.0%
Burnley	430	591	42.1%	439	590	42.7%	0.5%
Chelsea	564	415	57.6%	552	421	56.7%	-0.9%
Crystal Palace	440	526	45.5%	446	525	45.9%	0.4%
Everton	483	503	49.0%	482	499	49.1%	0.2%
Hull	434	503	46.3%	436	496	46.8%	0.4%
Leicester	457	557	45.1%	459	554	45.3%	0.2%
Liverpool	584	419	58.2%	581	430	57.5%	-0.8%
Man City	670	384	63.6%	658	392	62.7%	-0.9%
Man United	513	386	57.1%	506	387	56.6%	-0.4%
Newcastle	466	442	51.3%	470	448	51.2%	-0.1%
QPR	534	621	46.2%	545	633	46.2%	0.0%
Southampton	510	384	57.0%	505	386	56.7%	-0.3%
Stoke	503	455	52.5%	501	454	52.4%	-0.1%
Sunderland	409	625	39.6%	420	612	40.7%	1.1%
Swansea	427	551	43.7%	431	542	44.3%	0.7%
Tottenham	526	489	51.8%	530	495	51.7%	-0.1%
West Brom	413	546	43.1%	414	534	43.7%	0.6%
West Ham	487	589	45.3%	490	589	45.4%	0.1%

2nd half of Season (Matches 20-38)

Team	Total Shots For	Total Shots Against	TSR%	Schedule Adjusted Shots For	Schedule Adjusted Shots Against	Sched Adj TSR%	Dif
Arsenal	289	214	57.5%	282	218	56.4%	-1.0%
Aston Villa	226	208	52.1%	224	206	52.1%	0.0%
Burnley	215	287	42.8%	222	286	43.7%	0.9%
Chelsea	255	222	53.5%	253	227	52.7%	-0.8%
Crystal Palace	236	270	46.6%	240	268	47.2%	0.6%
Everton	236	256	48.0%	234	253	48.1%	0.1%
Hull	240	217	52.5%	241	216	52.7%	0.2%
Leicester	246	256	49.0%	246	255	49.1%	0.1%
Liverpool	294	199	59.6%	285	203	58.4%	-1.2%
Man City	350	182	65.8%	335	184	64.6%	-1.2%
Man United	262	177	59.7%	255	181	58.5%	-1.2%
Newcastle	230	209	52.4%	232	213	52.2%	-0.2%
QPR	264	328	44.6%	271	336	44.6%	0.0%
Southampton	256	211	54.8%	257	214	54.6%	-0.2%
Stoke	249	211	54.1%	252	216	53.9%	-0.3%
Sunderland	195	347	36.0%	202	335	37.6%	1.6%
Swansea	218	281	43.7%	222	275	44.6%	0.9%
Tottenham	274	251	52.2%	277	255	52.0%	-0.2%
West Brom	180	308	36.9%	182	296	38.1%	1.2%
West Ham	228	309	42.5%	232	307	43.0%	0.5%

1st Half of Season (Matches 1-19)

Team	Total Shots For	Total Shots Against	TSR%	Schedule Adjusted Shots For	Schedule Adjusted Shot Against	Sched Adj TSR%	Dif
Arsenal	319	192	62.4%	317	191	62.4%	-0.0%
Aston Villa	189	273	40.9%	186	269	40.9%	-0.0%
Burnley	215	304	41.4%	217	305	41.6%	0.2%
Chelsea	309	193	61.6%	299	196	60.4%	-1.2%
Crystal Palace	204	256	44.3%	204	257	44.3%	-0.1%
Everton	247	247	50.0%	250	246	50.4%	0.4%
Hull	189	286	39.8%	188	280	40.2%	0.4%
Leicester	211	301	41.2%	215	300	41.7%	0.5%
Liverpool	293	215	57.7%	301	221	57.6%	-0.1%
Man City	320	205	61.0%	320	209	60.6%	-0.4%
Man United	246	209	54.1%	246	206	54.4%	0.4%
Newcastle	236	233	50.3%	235	233	50.2%	-0.1%
QPR	269	293	47.9%	273	298	47.7%	-0.1%
Southampton	253	173	59.4%	248	173	58.9%	-0.5%
Stoke	254	244	51.0%	250	242	50.8%	-0.2%
Sunderland	214	277	43.6%	218	276	44.2%	0.6%
Swansea	209	269	43.7%	213	265	44.6%	0.8%
Tottenham	252	238	51.4%	250	242	50.8%	-0.6%
West Brom	233	238	49.5%	232	237	49.5%	-0.0%
West Ham	259	275	48.5%	258	274	48.5%	0.0%

Macro look at combined Attack/Defense in the Premier League

As we come down the stretch, I wanted to take a quick look at the shots and goals numbers for & against. Lots of times we only see the Shot Ratios (i.e. TSR, SoTR, GF%) posted but they do not give us the actual quantity of shots each team has taken and given up.

I know scatter plots can be tough to read/decipher but, for me, this is the best way to show where teams stand.

*This is the 3rd post of 3. The 1st post covered Attack and the 2nd looked at the Defense. 

Attack & Defense Combined

Shots on Target Differential vs Total Shots Differential

We can see that their is a clear top 5 teams with Man United lagging behind. The race for the 3rd and 4th Champions League spots looks like they could come down to the last game of the season.  Extremely tight.

Meanwhile, Sunderland are just abysmal. How they are not sitting at the bottom table is fascinating.

Everton, stand out as outliers, along with Chelsea, in over performance of shots on target. Chelsea are thriving off this, while Everton haven't been able to capitalize.

Goal Differential vs Shots on Target Differential

*If you are not familiar with PDO please check out the Glossary along the top of the page.

Analytics tell us to expect Man United's PDO to regress toward the mean (100). Many, mistakenly, think that if a team carries a very high PDO for a lengthy period of time that it will, in turn, have a game or multiple games where the team has a terribly low PDO. This is gamblers fallacy which many confuse with regression to the mean. 38 games is a small sample and good/bad fortune can last over the course of the season (or even more), even if it is unlikely. For example, Man City finished last season with ~112.0 PDO.

**Just for clarification, I am not saying Man United's PDO will stay high (It could fall off a cliff, for all I know). I am just trying to clarify that over the last 10 games (for all teams) anything can happen. The previous 28 games do not determine the PDO of game 29. Much like flipping a coin and getting heads 10 times in a row doesn't mean the odds of getting tails is higher on the 11th flip**


That is the end of this short 'series' of blogs. Hopefully, you found it somewhat informative and/or made you think about things a little different.

Cheers,

Clarke
Follow @footyinthecloud

 

Macro look at the 'Attacks' in the Premier League

As we come down the stretch, I wanted to take a quick look at the shots and goals numbers for & against. Lots of times we only see the Shot Ratios (i.e. TSR, SoTR, GF%) posted but they do not give us the actual quantity of shots each team has taken and given up.

I know scatter plots can be tough to read/decipher but, for me, this is the best way to show where teams stand.

*This is the 1st post of 3 (time permitting). This one will focus on Attack. The next will be Defense, followed by combining Attack/Defense and looking at Shot/Goal Difference.

Attack

Shots on Target For vs Total Shots For

Who had QPR leading Man United in 'total shots for' before the season started? Problem for QPR is that they just do not hit the target. Some of this will be down to poor shot selection and some down to being unlucky but I think we can make a guess that QPR (and Stoke) start hitting the target at a more normal rate over the last 10 matches.

Chelsea continue to lead in 'shots on target' but Man City has made gains in recent weeks. I have been expecting Chelsea to regress to a normal rate of putting shots on net but this has not happened. Their ability to get into dangerous shooting areas has been very good. As per Objective Football, Chelsea leads the league in 'minutes played while leading' by a lot of minutes and this is probably driving the disparity between shots and shots on target.

Man United and Southampton really do not have 'Top 4' shot numbers. Meanwhile Spurs seem to be moving on up in the shots for department.

Goals For vs Shots on Target For

Liverpool really should have more goals, at this point, based on their shots on target. Kind of surprising given their solid run of form over last couple months. I cannot really see any reason why the goals will dry up, other than injuries.

Aston Villa cannot score goals this season (~18% G/SoT). Can 'Tactics Tim' simply say 'Benteke, score me some goals'? Worked for Mr. Burns on the Simpsons Episode 'Homer at the Bat' (Could not, for the life of me, find a video clip):

Burns: You, Strawberry, hit a home run.

Strawberry: Okay, skip.

(hits a home run)

Burns: Ha-ha! I told him to do that.

Smithers: Brilliant strategy sir.

If Villa start scoring it will be more to do with regression rather than Sherwood waving his magic wand. I am sure the media will see it this way, as well.

I mentioned, above, that we could make a guess that Stoke will start hitting the target more often. We can also make an assumption that they will score at a slower rate. Now this does not mean they will not score. It just means that they will score at more normal rate. Essentially, the increase in shots on target will offset the decline in scoring %. Confused? Me too.

Man, stats can be Hard to Expain...



Clarke
Follow @footyinthecloud

What are we looking at?

What are we trying to find by looking at on pitch statistics at an individual player level?

Shots vs. Goals

Goals

Their is a tendency for the coaches/media/fans to judge individuals on goals/assists for and against. The problem with using goals as a performance indicator is the fact that 'luck', or lack thereof, plays a massive part in scoring/conceding goals. Read these 2 links on PDO. Link1 Link2

It is my experience, from hockey analytics, that players and teams with a high PDO get a lot more 'praise' than players/teams with a low PDO. 

High or low on pitch shooting %/Save% are not sustainable and will regress toward the average.

Shots

The more repeatable stat for determining performance is using total shots. Here is a great article on shot quantity vs. quality. If we can find out which players help increase shot differential, we can then increase the teams chance of scoring/saving a goal by playing them more or subbing them at the right times, therefore, leading to more points in the league table.

What I am looking for, are players who help increase total shot differential, of their team, while on the pitch. The higher the TSR% the more we know what part of the pitch the game was played in while a player is on the pitch.  Raw on pitch TSR% is okay but does not provide context.  What we see is that good teams have players with high TSR% and bad teams have players with lower TSR%.

The following stat allows us to differentiate players on the same team.


Relative Total Shot Number per 90 (TSR Rel/90) = Total Shot Number of player - Total Number of Team when player not on pitch

TSR Rel/90 begins to tell us who 'drives play' on his particular team.  Helping create a shot for or stopping a shot against are equally important. Over the course of the season we will be able to see who has impacted shot ratios. Obviously, the higher the number the better.

What does a TSR Rel/90 of 1.25 mean? It is saying that for every 90 minutes that a player plays 11v11 versus every 90 minutes that he doesn't play, his team will be better off by a total shot differential of 1.25 per 90.  The higher the number the better.


In Closing

It is by no means a 'perfect' stat. As always, their are question marks.  With 11 players on the pitch their is a lot of noise. The quality of the competition faced by each player, while he is on the pitch, is not accounted for. Game states or score effects are not taken into account. I just do not have the data and time integrate these into the analysis.

The general idea is to find a new way of analyzing players by removing goals from the equation. If we find a player who is very high or low we can look into his performances by watching film and looking at the quality of competition, quality of teammates, game states etc.




Clarke Ruehlen
Follow @footyinthecloud