Wednesday, 25 June 2014

How To Use Total Shot Ratio To Predict And Bet On Football Matches

Total Shot Ratio has emerged as a tool for quantifying the strength of soccer teams; this article outlines what TSR is and discusses its strengths and weaknesses.

The emergence of Total Shot Ratio
Predicting the outcome of football matches initially relied exclusively on the only readily available data, namely goals. Once applied to a Poisson approach, the ability to forecast individual goal probabilities for each side enabled a wide variety of associated events to be framed, from correct scores to the timing of goals.

Other sports, notably in North America, had access to a much more varied and extensive collection of events. In the NHL, shooting data outnumbered the more valuable, but much less frequent act of scoring of a goal. This led to the additional use of shots, as well as actual goals, as a way of describing the likely difference in ability between teams.

Better sides tend to produce the more offensive output – hence more shots – and in turn face fewer opposition attempts. Total shot ratio – or TSR for short – finally made the crossover from hockey to soccer once shooting data became more readily available in the latter sport.

What is Total Shot Ratio & how to calculate it?





TSR measures the proportion of shots a side contributes (off or on target including headers) in a single match or an entire season. Therefore, if a contest between Team A and Team B contained 30 shots and team A contributed 20 of the attempts, team A’s TSR would be 20/30 or 0.67, with Team B naturally recording a value of 10/30 or 0.33.

Larger samples more quickly tend towards their true values, as the talent begins to shine through. Total match goals in the major soccer leagues rarely stray from an average of around 2.5 per game, while shots invariably reach well into double figures. Hence, it could be expected that the share of shots, rather than goals could better reflect a side’s true talent.

For TSR to be useful as both a predictive tool as well a descriptive one, it needs to correlate well with results. Teams with good TSR values should also perform well in terms of wins and league points. In addition, historical TSR should predict future TSR reliably, in the same way that a weighted sample of goals scored and allowed can be used to predict future scoring rates.

League points do appear to correlate fairly well to a side’s TSR for that season. Below we’ve plotted league points and team TSR for 10 seasons of English Premier League soccer from 2002-03 to 2011-12 and there does appear to be a correlation between the two. A side with high TSR’s over a season tended to gain more points than those with poorer ones.


Similarly, TSR begins to approach the end of season values relatively quickly. Mid-term TSR values show a very strong relationship to the TSR recorded by a team at the end of the season. Team TSR from one sequence of games is also reasonably well connected to subsequent batches of games.

So, we should be able to use the TSR recorded by two teams over a prior run of matches as a proxy of their likely respective abilities going into a future match.


To try out TSR as a potential talent rating for individual teams, we recorded the overall TSR recorded by EPL teams in their previous 20 matches and used these figures as the rating each side took into a future game. We then compared the actual results of matches using each team’s TSR as the two predictors and a strong relationship appeared to exist.

This relationship was then used to produce win, lose and draw odds for future matches, examples of which from the perspective of the home side are included below. Games were played in the latter stages of the 2012/13 English Premier League season and we’ve included the odds from that day.

Match
Home Team TSR
Away Team TSR
Predicted Home Win
Quoted Implied Probability
Newcastle vs Fulham
0.505
0.447
0.50
0.48
Tottenham vs Everton
0.650
0.586
0.50
0.46
Man Utd vs Man City
0.540
0.640
0.16
0.43
Arsenal vs Norwich
0.607
0.433
0.77
0.80
Stoke vs Aston Villa
0.416
0.387
0.43
0.48
Chelsea vs Sunderland
0.570
0.422
0.72
0.73
Liverpool vs West Ham
0.621
0.440
0.78
0.74

For most of the games, a TSR based approach produces comparable home win odds to those quoted, indicating their possible use as a tool to frame match odds, with one glaring exception. Manchester United was given a much smaller chance of defeating their cross City rivals than was generally quoted.

To calculate Predicted Home win
We used the TSR over the previous 20 matches for the home and away side and whether or not the match was a home win (or away win) for three seasons.

Therefore the two-predictor variables were home TSR and away TSR and the outcome variable was coded 1 for a home win or 0 for any other result.

That gives you your regression equation, which you can use to input future TSR match ups to give the probability of in this case a home win. That’s the “Predicted home win” column.

To calculate Implied odds
The “Quoted Implied odds” is Pinnacle Sports’ odds for a home win, expressed as a percentage, prior to the game.

Limitations of Total Shot Ratio
A side can over-perform against their fundamental shooting statistics simply through random variation, but United have often performed in such a manner when managed by Sir Alex Ferguson. Therefore, whilst a long lucky streak is possible, if improbable, a more likely explanation is that TSR fails to capture everything about a side’s talent.

A further clue concerning the limitations of TSR can be seen in the under rating of Stoke at home to Villa. Stoke – at the time were managed by Tony Pulis – who prospered in the English Premier League by creating fewer, high quality chances – often from set plays and long throws – were happy to allow their opposition to have a higher numbers of low quality attempts, often from distance.

TSR does not discriminate between speculative longshots and close range chances, or even shots widely off target, but reality and location based shot models do. Therefore, some teams may use repeatable tactical wrinkles that are not picked up by a method based around shot volume and may be consistently over or under rated because of this.

Few teams play in such an extreme defensive shell as Stoke formerly employed and so the majority of sides will see their TSR more accurately reflect their talent base and single abnormal, luck driven seasons will likely be the exception. Therefore, for those looking to a viable alternative to using goals, TSR may fit the bill, as long as they allow for a tactical maverick operating among a more mainstream approach.



Wednesday, 4 June 2014

How To Find A Tennis Betting Edge

This tennis betting article explains how to find an edge on grass courts. Read more to assess the abilities needed for grass court success, and details of which players have strong and weak grass court records on the ATP Tour. Knowing this information could be the difference between making a profit or not.

What are the characteristics of a good grass court player?
As mentioned in previous articles, the surface characteristics of grass are very different to that of clay, and as a quick refresher, some comparable ATP Tour statistics are detailed below:

Surface
12 Month Service Hold
12 Month Opponent Break
12 Month Aces Per Game
12 Month Break Points Per Game
Clay
75.6
24.4
0.35
0.59
Grass
82.4
17.6
0.61
0.49
Overall
78.6
21.4
0.49
0.55

As can be seen from the above table, the average ATP player holds serve 6.8% more on grass than clay, achieves 0.26 more aces per game and faces 0.10 fewer break points per game.

Based on the above statistics, as well as knowledge of the players, it’s logical to assume that grass courts, being the fastest surface on tour, benefit those players who have a big serve. Good net skills, with the serve/volley style predominant on grass, are also very desirable, as well as the ability to play good drop shots.
These qualities are often quite alien to the traditional clay courters who have weak serves and tend to prefer playing from the baseline on slow surfaces. On that basis, it’s reasonable to assume that a large number of these players struggle to play on grass, and the appearance statistics of numerous top 100 players bear that out, with some rarely featuring on grass in the European summer months.

The likes of Federico Delbonis, Joao Sousa, Pablo Carreno-Busta, Alejandro Gonzalez, Dusan Lajovic, Blaz Rola and Victor Estrella Burgos can probably be excused not playing frequently on the surface due to their fairly recent ranking rises, which previously would not have seen them eligible for main draw matches in recent years. Having said that, it’s fairly likely that none of these players have the necessary tools to be successful grass courters.

Players such as Albert Montanes (3 grass matches in the past 3 years), Nikolay Davydenko (3), and Filippo Volandri (4) have less excuse. It’s clear they have a dislike for the fast surface, and Davydenko – after his first round French Open exit to Robin Haase – has already said he will skip the entire grass court season.

However, there are a number of players who are much more adept on grass, and show considerable improvements on their overall stats on the surface. The following table details the players who have strong grass court records, compared to their overall records, in the last 3 years (data correct at 28th May, 2014):

Player
Rank
3 Year All-Surface: Service Hold %
Break Opponent %
Combined %
3 Year Grass Service Hold %
Break Opponent %
Combined %
Difference
Kubot
63
74.3
22.5
96.8
83.3
22.4
105.7
8.9
Mahut
40
80.9
19.7
100.6
86.8
22.5
109.3
8.7
Mannarino
89
73
19.7
92.7
79.2
21.9
101.1
8.4
Becker
69
74.8
17.3
92.1
80
20
100
7.9
Hewitt
46
75.9
24.6
100.5
83
24.7
107.7
7.2
De Schepper
72
82.3
10.3
92.6
86.8
13
99.8
7.2
Lopez
27
84.8
15.7
100.5
92.4
14.9
107.3
6.8
Janowicz
23
83.7
17
100.7
91.4
15.7
107.1
6.4
Tomic
80
79.7
17.5
97.2
86.5
16.2
102.7
5.5

There were nine players in total in the top 100 that had a grass court combined hold/break percentage more than 5% bigger than that across all surfaces.

Leading the way is Lukasz Kubot, who has very strong grass court stats for his current ranking. He made the quarter-finals of Wimbledon last year, and his conqueror, Jerzy Janowicz, also made this list. Both Poles can consider this their best surface.

Nicolas Mahut, whose serve/volley playing style makes him a very strong player on the surface, had the second biggest difference on his way to two grass court titles, whilst Adrian Mannarino, who is defending last 16 points at Wimbledon, was third.

Veteran Lleyton Hewitt was also impressive in 2013, reaching the semi-final of Queens and losing the final to Mahut in Newport. With a 38-12 record in his last 50 at Wimbledon, he will be one of the unseeded players that no seed will wish to face, along with Mahut. Eastbourne champion Feliciano Lopez also made the list, and also has compiled an impressive record (32-18) in his last 50 on grass.

The following table details the players who have poor grass court records, compared to their overall records, in the last 3 years (data correct at 28th May, 2014):

Player
Rank
3 Year All-Surface: Service Hold %
Break Opponent %
Combined %
3 Year Grass Service Hold %
Break Opponent %
Combined %
Difference
Andujar
78
68.8
25.3
94.1
55.7
12.4
68.1
-26
Matosevic
66
73
22
95
63.6
16.5
80.1
-14.9
Nadal
1
86.1
34.4
120.5
85.4
23.3
108.7
-11.8
Gimeno-Traver
94
74.6
15.8
90.4
73.3
6.2
79.5
-10.9
Hanescu
85
77.6
18.5
96.1
72.6
13.2
85.8
-10.3
Berlocq
47
73.2
28.3
101.5
79.6
15
94.6
-6.9
Wawrinka
3
83.6
24.8
108.4
84.4
17.3
101.7
-6.7
Gulbis
17
82.9
21.6
104.5
87.6
10.6
98.2
-6.3
Mayer L
65
78.8
18.6
97.4
76.9
14.4
91.3
-6.1
Robredo
19
79.2
23.9
103.1
82.8
14.3
97.1
-6
Nieminen
60
75.6
23.1
98.7
81.3
11.7
93
-5.7
Ebden
73
70.8
19.2
90
71.9
12.4
84.3
-5.7
Simon
30
76.2
27.2
103.4
78.2
19.7
97.9
-5.5

What is immediately obvious is how many clay courters make this list.

Andujar, Nadal, Gimeno-Traver, Hanescu, Berlocq, Mayer, Robredo and Simon can all consider clay to be their best surface and clearly do not enjoy playing on grass. Andujar, who by some distance was the worst player on the list, has failed to win a match in seven attempts on the surface in the last 3 years.

What may be of some surprise to readers is the appearance of Nadal on the list, at third. The king of clay has two Wimbledon titles (2008 and 2010) to his name in the last ten years and has also been runner-up three times. However, in recent years he has unimpressed on grass, losing at Wimbledon to Steve Darcis priced at 1.011 in 2013. In 2012 he was defeated by Lukas Rosol priced at 1.008, as well as losing in Halle to Philipp Kohlschreiber, at 1.149. What is also worth mentioning is that Nadal failed to play a warm-up grass event prior to Wimbledon last year, and was clearly under prepared for the surface change from his beloved clay.

Also worth mentioning is Stan Wawrinka, who finds himself at 7th in this list. A previous article illustrated that the Australian Open champion clearly has a distaste for fast surfaces, and his individual grass court stats seen above back that point up well. The Swiss player could be vulnerable as a heavy favourite on grass this season.
The above statistics clearly show that detailed research of surface records is something that a successful bettor needs to have in their armoury and is a vital facet of a balanced betting strategy.