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I came across this strategy when reverse engineering Betfair starting price bets. It came to my attention that certain horses had very similar patterns in terms of starting price bets (same odds limit, always the same time SP bets are placed etc). After further investigation I then discovered that these were horses that have not participated in any race previously. With this I defined a very simple betting strategy for a backtest: Back bet on horses making their debut using the Betfair Starting Price (BSP).
Backtest Period: 01 Jan 2014 to 08 Oct 2020
Total Profit: +2,987.38 Points
Average Profit: +36.25 Points per Month
Number of bets: 47,129
Max. Drawdown of 905.74 Points during 573 Days
Lay the draw is probably the most popular trading strategy for inplay football betting markets. Some readers reported having some success when applying this strategy in the past. Based on this, we conducted a more systematic backtest to see if this popular approach is still profitable.
Backtest Period: 18 Jan 2020 to 01 Feb 2020
Total Profit: +5.31 Points
Average Profit: +12.24 Points per Month
Number of bets: 1,909
Max. Drawdown of 8.95 Points during 6 Days
Machine Learning and Artificial Intelligence are powerful tools to learn from large amounts of data and help to make better decisions. In this article I would like to train a machine learning model that is capable of predicting the outcome of football matches.
Backtest Period: 08 Apr 2019 to 12 May 2019
Total Profit: +5.37 Points
Average Profit: +4.74 Points per Month
Number of bets: 56
Max. Drawdown of 6.96 Points during 9 Days
After observing a couple of UK / IRE horse racing markets on Betfair, we noticed that shortly after the creation of the market lay bets were placed in the range between 1.01 and 1.07.
Backtest Period: 01 Jan 2014 to 10 Jan 2020
Total Profit: +238.50 Points
Average Profit: +3.25 Points per Month
Number of bets: 74,726
Max. Drawdown of 8.05 Points during 41 Days
This betting system involves backing the draw in football matches for games without a clear favourite, i.e. both teams have similar odds. We would like to share the Python code that we used to test the strategy as well as the backtesting result.
Backtest Period: 18 Aug 2012 to 12 May 2019
Total Profit: +51.31 Points
Average Profit: +0.63 Points per Month
Number of bets: 436
Max. Drawdown of 24.01 Points during 453 Days
This is a very simple strategy where a back bet is placed on underdogs who are playing at home. A team qualifies as underdog when the odds for a win are higher compared to the opponent. Backtest is done for major football leagues in Europe but the strategy could also be applied to other sports.
Backtest Period: 17 Aug 2013 to 12 May 2019
Total Profit: +37.63 Points
Average Profit: +0.54 Points per Month
Number of bets: 2,280
Max. Drawdown of 38.74 Points during 441 Days
This betting system focuses on the German football league, Bundesliga, and involves betting against the favourite. There are various sets of rules that can be applied in order to increase profitability. Some of them are selecting home favourites only or excluding certain odds ranges.
Backtest Period: 03 Aug 2012 to 19 May 2019
Total Profit: +17.79 Points
Average Profit: +0.22 Points per Month
Number of bets: 2,332
Max. Drawdown of 60.14 Points during 511 Days
A betting strategy to predict the outcome of football matches based on the Dixon-Coles model is evaluated on historical data using Python code. The Dixon-Coles model to predict the outcome of football matches goes back to a scientific publication in the year of 1997. Back then Dixon and Coles, the authors of the paper, published a mathematical model that would allow to predict the result of football (soccer) matches.
Backtest Period: 03 Nov 2018 to 12 May 2019
Total Loss: -26.93 Points
Average Loss: -4.25 Points per Month
Number of bets: 280
Max. Drawdown of 37.27 Points during 88 Days
How profitable is it to bet against favourites in horse races? This strategy is about laying favourites in horse racing markets on a betting exchange.
Backtest Period: 31 Dec 2012 to 08 Oct 2020
Total Loss: -433.10 Points
Average Loss: -4.58 Points per Month
Number of bets: 76,944
Max. Drawdown of 539.66 Points during 2,280 Days
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