Analysis of the In-Play Soccer Spreadsheets (Part One)


With the Tennis season taking a break for the off-season, I thought it might be interesting to do some analysis on the In-Play Soccer Spreadsheets and evaluate how they have performed in the first four months of the season.

The first part focuses on goal expectation based on both Combined Score (see below for calculation) and starting price range.  In the second part, I look at creating a model for both the Both Teams To Score markets, and the Over/Under 2.5 goals markets.

For the first part, I created a database which contained the following information:-

Price Range Based on Starting Odds
Combined Score From The In-Play Soccer Spreadsheets (Home Team Goal Lead Lost % + Home Team Goal Deficit Recovery % + Away Team Goal Lead Lost % + Away Team Goal Deficit Recovery %)
Both Teams Score Yes/No
Match Total Goals 

The sample contained all matches in the six top-flight European leagues covered by the Soccer Spreadsheets (these also include the English Championship & Brazilian Campeonato).  Filtered out were matches involving newly promoted teams, due to small sample size of data.

This created a 620 match sample which I then analysed in some detail, with the following results.

FAVOURITE MATCHES MEAN BTTS OVER 2.5 MEAN
SP CS % % GOALS
<1.50 76 162.05 44.74 61.84 3.16
1.50-1.64 68 165.68 57.35 58.82 2.93
1.65-1.79 62 154.21 35.48 51.61 2.50
1.80-1.99 88 156.98 56.82 51.14 2.88
2.00-2.14 52 164.17 55.77 44.23 2.65
2.15-2.29 69 158.36 53.62 44.93 2.57
2.30-2.50 82 154.98 50.00 48.78 2.52
2.50+ 123 152.23 48.78 44.72 2.48

We can see that as the starting price of the pre-match favourite gets towards 3.00, the goal expectation in a match generally decreases.  Heavy starting price favourites in the first two price range brackets (overall starting price 1.64 or below) had the highest match goal expectation with matches involving favourites priced below 1.50 featuring 3.16 goals on average.

Interestingly, whilst mean goal expectation for matches involving a favourite priced under 1.50 was very high, the mean both teams to score percentage was low at 44.74 (sample mean was 50.00%, as shown in the next table).  Clearly these matches produced a high number of dominant scorelines which favoured the 'better' team.

Just as interesting is that whilst the mean goal expectation for favourites with a starting price of even money or bigger was significantly reduced from heavy favourites, and was below the sample mean of 2.70 goals, the both teams to score percentage was higher than for that of favourites considered more likely to win by the market.  Logically, this would lead to a number of 1-1, 2-1 and 1-2 scorelines in these areas, with the number of goals around the mean and both teams scoring.  

On this basis, it would appear that the Both Teams To Score bets and Over/Under 2.5 goals bets are not created at all equally.  There certainly would be many occasions where backing both teams to score would be appropriate, but over 2.5 goals would not be, and vice versa.  This is very similar to pre-match handicap propositions in Tennis, where backing a player on the set handicap might be a much better position to take than backing them on the game handicap, for example.  The market doesn't always take this into account, and I see many bettors bemoaning the fact they should have gone for one bet over the other, without any logical reason.

The next part of analysis that I performed was on how the spreadsheets performed based on the combined score generated for each match:-

CS MATCHES BTTS OVER 2.5 GOALS
<90 22 50.00 40.91 2.55
90-109 18 38.89 33.33 2.44
110-125 39 41.03 41.03 2.33
126-140 79 43.04 46.84 2.37
141-155 118 50.00 46.61 2.58
156-170 124 54.03 52.42 2.61
171-185 112 50.00 53.57 2.99
185-199 71 49.30 52.11 2.93
200+ 37 67.57 72.97 3.35
OVERALL 620 50.00 50.32 2.70

The findings here were stark.  It can be seen that there is a clear relationship between combined score and the Both Teams To Score and Over 2.5 percentage.  Not only this, it also had a clear relationship with goal expectation.

When combined score was 140 or below (the first four brackets), BTTS percentage was 43.04% (68/158) but when combined score was 156 or above (the last four brackets), BTTS percentage was 53.20% (183/344) - a large increase of 10.16%.

Looking at the same 140 or less and 156 or more brackets, Over 2.5 goals percentage was also 43.04 (68/158) for the first four brackets but was slightly higher than BTTS for the last four brackets at 54.94% (189/344) - a slightly bigger increase of 11.90%.

Figures for the 200+ combined score were especially fascinating.  Whilst the 37 match sample wasn't great, 25 matches had both teams scoring and 27 matches went over 2.5 goals.  The 3.35 mean goal amount was significantly higher than any other bracket.

It is clear that teams who are more likely to recover and concede goal deficits/leads - information which the In-Play Soccer Spreadsheets provide - have a higher than average goal expectation, and also produce more matches where both teams score and total goals are over 2.5.  

There can be little doubt here that goals in these matches accelerate further goal expectation and this information can be used both pre-match and in-play.

In part two, I will look at creating a model based on this information, and assess the ROI of the Soccer Spreadsheets based on that.




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