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30th January, 2017.
The ultimate measure of the success of a model is how it performs against the Pinnacle Sports closing lines. With big limits and no restrictions for successful bettors (both unlike conventional bookmakers), Pinnacle is a very efficient bookmaker, and its market activity forms a large proportion of pre-match action.
We have seen via the pre-match betting performance data that my model can beat Pinnacle by around 3-4% ROI at the prices in non-first round when my daily spreadsheets are compiled, purely for match win odds. It is very difficult to beat Pinnacle by significantly more than this – particularly if you are betting close to the match start time (closing lines).
If you are beating the pre-match win markets by over 5%, you are absolutely killing it.
However, I wanted to see if there was a further angle I could look, with a view to seeing if I could squeeze several more percent out of the market.
I decided to look at how ATP players, when considered value by my model (as indicated on the TennisRatings daily betting/trading spreadsheets) in non-first round matches covered the closing game handicap lines for the last two completed seasons, 2015 and 2016.
You can download the file below which shows the full handicap data for ATP value players at the closing game handicap line closest to evens with Pinnacle, at the match start. Given that all game handicap bets were analysed at prices around evens, a flat staking strategy was hypothetically used.
The results are summarised below:-
|
Value Player SP to Win Match |
Matches |
Stake |
P/L |
ROI % |
|
1.00-1.49 |
153 |
15300 |
1229 |
8.03 |
|
1.50-1.99 |
180 |
18000 |
805 |
4.47 |
|
2.00-2.99 |
261 |
26100 |
2088 |
8.00 |
|
3.00-4.99 |
181 |
18100 |
-918 |
-5.07 |
|
5.00-7.99 |
77 |
7700 |
298 |
3.87 |
|
8.00+ |
47 |
4700 |
748 |
15.91 |
|
Overall |
899 |
89900 |
4250 |
4.73 |
|
Favourites |
333 |
33300 |
2034 |
6.11 |
|
Underdogs |
566 |
56600 |
2216 |
3.92 |
|
1.00-2.99 |
594 |
59400 |
4122 |
6.94 |
Overall, the sample yielded a very strong 4.73% ROI from 899 matches, when all value player starting price ranges were included, and all price ranges were positive with the exception of the 3.00-4.99 bracket.
Favourites performed stronger than underdogs – backing favourites to cover the game handicap line at closing game handicap prices had a superb 6.11% ROI from 333 matches, with underdogs generating a lower, but still worthwhile, 3.92% ROI from 566 matches.
If we pick value players with starting match win prices below 3.00 (1.00-2.99), this yielded a spectacular 6.94% ROI from 594 matches, and followers of my model could consider backing these players on the game handicap markets for a larger stake than underdogs priced at 3.00 or greater.
Concluding, it would appear that market mistakes which are highlighted by my model recommending value on a player are magnified using the game handicap markets, and we can generate an extra percentage point or two in ROI by looking in these markets as opposed to the match win markets.
