Before betting on draws, I needed to spot overpriced odds to get value from Market misjudgments.
So, I had to compile my odds to bet on higher prices than I estimated.
To get started, I just needed a KISS.
A “Keep It Simple, Stupid” approach was my starting point to compile the odds from an unemotional and straightforward perspective.
People who run ball clubs, they think in terms of buying players. Your goal shouldn’t be to buy players. Your goal should be to buy Wins. And in order to buy wins, you need to buy Runs.
Replace the word “Runs” with “Goals”, and that’s it.
In order to buy Wins, Football clubs need to buy Goals.
Goals scored and prevented.
If you want to get Wins, you have to score one goal more than the opponent and get the three points to advance in the ranking.
Where do most of the goals come from?
85% of the time, they come from shots in the box.
Thus, a football team is focused on taking as many shots in the box as possible and avoiding shots in the box against.
So, to evaluate a team, I used the Total Shots in the Box Ratio (TSBR).
TSBR = ShotsintheBoxFor / (ShotsintheBoxFor + ShotsintheBoxAgainst).
I started doing this by collecting data from the 2011/2012 season to calculate the TSBR for each team at home and away.
At this point, I had to group the teams according to their TSBR rating to know how the results usually play out when teams play with one another. In this way, I had the number of wins, draws, losses, and the average expected goals for each fixture type.
With the average expected goals of any game, I used the Poisson distribution to compare wins, draws and losses percentage with those calculated by the TSBR.
Thanks to the complete picture that I had available for each fixtures types, I could start to backtest and build my value betting strategies verifying the hypotheses that came out by observing the expected number of draws of each match.
Looking back for moving forward
The advantage of betting on draws comes in many ways.
Not only from the possible mistakes of the bookmakers.
Thanks to some typical behaviour I mentioned in the first part of my investment thesis on football draws, even amateur bettors, big spenders, and pro tipsters can make draws a value investment.
Their behaviours have survived these days.
They keep moving in their comfort zone by betting on winners.
The nature of behaviours has not changed over time, and I think it will continue to shape the future.
Therefore, you need to pay attention to the past to observe what has survived.
The past is the best indicator to develop long term value betting strategies on draws, just like it happens in the business world as Warren Buffett points out:
The rearview mirror is always clearer than the windshield.
How did I build my Value Betting Strategies?
I had several hypotheses to test, thanks to the analysis I performed after compiling my odds.
I had a large sample size made up of eight football seasons.
I had compiled my odds on the TSBR, an objective metric for evaluating the team performance.
It was a good starting point for avoiding the risk of Overfitting when building betting strategies.
It occurs when a strategy is so complex and filled with too many parameters and variables to adapt your data in a customized way.
Overfitting also occurs when you create rules which are specific to the dataset you use in an attempt to increase profitability.
To avoid overfitting, you need to go through two more steps by keeping the strategies simple and by cross-validation.
In the first case, you have to consider a few rules to avoid the noise in the training data. Secondly, you take random subsamples from the entire dataset to perform observations with different subsets.
For example, you can exclude the most recent seasons from the whole sample and verify how the strategies have worked in the far past. Then, the strategies must have worked in the recent past to prove survival to these days by cutting the oldest seasons and considering those excluded in backtesting.
Also, another goal is not to fall into the Cherry-picking trap.
Cherry-picking occurs when figuring out specific spots to confirm a particular hypothesis while discarding a significant portion of related data that may contradict that hypothesis.
So, I tested on all football leagues to prove large-scale dissemination of my ideas and the same I did involving all the teams for each league to validate a strategy.
Finally, when building a betting strategy beyond an underlying theory and having a large sample size, you need consistent results over time, guaranteed by a high number of bets per year.
To make these observations more accurately, I used a database with more than 100,000 matches played in the top 50 leagues from 2012 to 2019.
About this, I would like to show the graphs of some strategies I have developed so far, with the most representative leagues.
High Volume Strategy
Low Maximum Drawdown
High Yield Strategy
Where do the results come from?
The results of my strategies come from the Pinnacle closing odds.
When you have results calculated on those closing odds, it’s like having a hallmark on the true final prices of a football game because they represent the most efficient odds on the market; since they are shaped by the weight of the money wagered by the sharpest bettors before the kick-off.
Pinnacle allows sharp bettors to wager a higher amount of money.
It is a bookmaker with a low margin operative model with increasing betting limits until the kick-off. It attracts high-level bettors with large bankroll whose bets lead to efficient odds on the market closing due to this smart volume of money that moves the odds line towards real prices.
Once you have underlying hypotheses, a large sample size and consistent results years-by-years, your strategies are more likely to succeed for a long time into the future.
I have to admit it was a funny and exhausting process at the same time.
It was like taking pictures in a long photography session.
I observed the draws from different angles with new eyes for every shoot.
It was like taking snapshots of a landscape at different times of the day.