Back when i was doing my internship, many years ago, the husband of a colleague of mine used to be into sports betting and would make a decent sum every month with this theory of his. Basically, he tracks the odds of a match from the time they're released till just before they close (in-game bets not included).
His theory was that you want to bet on the team with falling odds. With one caveat, if the odds drop too much, that indicates a 'heavy boat' and is a match that's likely to be fixed.
I've got no idea how he filtered which matches to bet on but for our purposes, let's set the following rules:
| Match | Prediction | Odds | Outcome |
|---|---|---|---|
| Al Riffa vs Al Shabbab | Al Riffa | 2 | L |
| Real Oviedo Vs Valladolid | Real Oviedo | 2.04 | L |
| Union Espanola vs Huachipato | Union | 2.1 | W |
| Ebbsfleet vs Sutton United | Ebbsfleet | 2.2 | L |
| SJK vs Honka | SJK | 2.39 | W |
| Boulogne vs Pau | Boulogne | 2.2 | L |
| Kyoto vs Kumamoto | Kyoto | 2.04 | L |
| Tokyo Verdy vs Mito Hollyhock | Tokyo | 2.1 | W |
| Queen of the South vs Dundee | Dundee | 2.2 | W |
| St. Mirren vs Greenock | St. Mirren | 2.04 | W |
So overall, we got 5/10 correct, which is better than E(X) of 3 or 4 out of 10. Given that we're looking at odds over $2, getting a 50% win ratio is needed to break even / make a small profit.
This round's return was 5.4% which is not worth the risk in my opinion. For the next round of 10, i'm going to modify the filter by using a form filter instead of historical / manager H2Hs.
