Creating A Sports Betting Model

• Creating A Sports Betting Modeling
• Creating A Sports Betting Model
• Sports Betting Algorithm Ideas
• Baseball Betting Model

If you are looking to create a sports betting model in order to book action on sports then the best and most efficient way would be to get set up with a pay per head service. These services provide a turn-key solution that anyone can use to book action and create their own sports betting website model.

I build analytics models in Microsoft Excel that analyze sports betting lines. My models update daily and all members can view them, purchase them, or better yet, help me make them better! Current models include NFL, NCAAF, NBA, NHL and MLB (Soccer on hold) NFL 2019: 101-61 MLB 2019: +65 units. NCAAB 2019: 241-239 NHL 2019: 43-33-2. How to build a betting model? Step 1 - Define the target/aim of the model. What are we trying to achieve? The aim is to create a projection for an NBA. Step 2 - Collect data. In theory data could be any numbers that have some link (correlation/explanatory power) to the. Step 3 - Construct the.

Research³ shows the most successful betting strategy is the Kelly Criterion. The Kelly Criterion states that each bet should be equivalent to the percent edge. For example, with an expected value of 0.109, or a 10.9% edge, the bet should be equivalent to 10.9% of the bankroll. However, this strategy is contingent on having an extremely accurate model where the expected edge is the true edge. Due to limited data of just two seasons right now, many of the expected values are above 0.2, which would be a ludicrous amount of the bankroll to bet on one game. So, all expected values are capped at 0.2 with this method.

Creating A Sports Betting Modeling

Starting with a bankroll of 100 units, the absolute maximum bet is capped at 40 because it became too easy to lose any sort of winnings while using the Kelly criterion.

Another system used modifications to the Kelly criterion system. In this system, the expected values essentially received a square root transformation; however, since the absolute value of the expected value must be less than one, this is actually a squared transformation. The new Kelly proportion is the expected value squared.

The Martingale betting system is another popular method. This betting system starts by betting a certain amount of units (in this case, 5 units). If the bet loses, the subsequent bet doubles, so that winning the bet leaves the bettor ahead by 5 units. If the second bet instead loses again, the third bet once again double – now to 20 units. This keeps on going until either the bankroll is empty or the bettor is ahead 5 units. This is a risky strategy.

One strategy simply bets 15 units any time there is a positive expected value. Another strategy only bets when there is an extreme edge; the bet is 10 units if the expected value is above 0.1 and 20 units if the expected values is above 0.2.

Finally, another betting system relied on the agreement of the two linear mixed models. If both models suggested betting on the same team, and each expected value is positive, then the system bets on this team. Betting amounts are determined with the Kelly Criterion, and the Kelly proportion is the average of the two different expected values generated from the different models.

The different betting systems are listed in Table 4.1.

4.1 Results Using K-Fold Validation

For each game in each test data set, the model went through the process outlined above for each betting strategy. After iterating through all the games in the test data set, I was left with the final bankroll from each model for each test data set in the k-fold. With 100 test data sets, I had a vector of 100 final bankrolls for each of the nine betting strategies.

Creating A Sports Betting Model

Figure 4.1 is a boxplot looking at the distribution of the final bankrolls for each of the betting strategies over the 100 simulations. Most of the strategies have medians above 100, meaning they generated positive median returns. In addition, these distributions have a boundary on the low end of 0, as the strategy cannot end with a bankroll below 0, but with no maximum, the distributions tend to be right-skewed, leaving the means higher than the medians.

Table 4.2 shows the mean and median final bankrolls over the 100 simulations for the different methods. Four of the methods stand out; the K1 method, Martingale, Extreme Edge - Simple Bets and the LMER Agreement method. Outside of the Martingale method, which is interesting because this is the only strategy where the mean is significantly lower than the median, the other listed methods are noteworthy because of the huge returns – each of those three methods have mean returns above 35%.

Table 4.3 displays the summary statistics for the 4 top methods; the team-specific model using Kelly Criterion for betting amounts, the Martingale method, the Extreme Edge with Simple Bets and the Agreement method. The highest median is this K1 method. All these methods used the team-specific model to generate probabilities of beating the spread. The returns are very high in each of the quartiles, with positive returns for over 75% of simulations for the Extreme Edge with Simple Bets and Martingale methods. However, these are both the riskiest methods, as in 5% and 15%, respectively for each method, the bettor would have lost the entire bankroll using these methods. This is a very high chance relative to most other more standard investments in the stock market.

Figure 4.2: Density Plots comparing Four Successful Betting Strategies

Figure 4.2 shows the density plots for the 100 final bankrolls from the top four methods. The dotted lines represent the medians of each distribution. There seems to only be 3 dotted lines, but the medians for both the K1 and Martingale methods are both 129, so these lines are overlaid. The LMER-Agreement method seems to provide the safest betting strategy, as the density plot does not have as long of a tail, but a larger peak close to 175 units. It makes sense that this method is safest because this method only chooses to bet on games that both models agree on for whom to bet. Thus, a more selective group of games is chosen for bets. The Martingale method has 15% of observations at 0, but based on the nature of the bets, which continue to double if the bettor is losing, if the bettor does not lose the entire bankroll, it is a near guarantee to make money. The K1 and Extreme Edge - Simple methods both have extremely long tails and provide riskier investments than the LMER agreement methods, but also higher mean and median returns.

The concept of a winning sports betting system seems simple. If you flip a coin to pick games, you can win 50% of the time. When you factor in the vig charged by bookmakers, you have to win 52%to 53% of the time, depending on the vig you pay, to turn a profit.

This means that to develop a winning sport betting system, you just need to be able to beat a coin flip by two to three percent. Surelywith the amount of statistics and metrics and computer power available in today’s world, this is a realistic possibility.

The 52% to 53% estimates listed above only consider spread betting and totals, but the concept is the same if you bet on money lines. The percentages are different depending on betting onunderdogs and favorites, but to make a long-term profit, you still only need to develop a system that operates slightly better than a coin flip.

Even though building a winning system seems like it’s possible, not many are able to do it. This page is designed to help you learn how to build a winning sports betting system.

They invest a great deal of resources and time into setting lines that can’t be exploited. They also try to determine what winning bettors do, and go to great lengths to make those winningsystems stop working.

I read an interview that a big sports bettor gave, and he described it as a race between him and the sportsbook. He constantly came up with ways to win and they constantly tried to eliminate hisedge.

His description is a good way to summarize your long-term battle with the bookmakers. A winning betting system is not a static thing. It has to grow and improve over time if you want to continuewinning.

Here’s an example:

Many NFL bettors remember when most home underdogs offered value. Smart bettors figured out that they could bet on every home underdog and turn a profit. The bookmakers were undervaluing theability of home teams in match-ups involving strong road teams.

This created a situation where a simple system (in this case, betting on home underdogs) was profitable. This was even written about at the time, which is one of the reasons it no longer works.

The bookmakers saw that a percentage of bettors were making consistent profit on home underdogs, and they started changing the lines. The books quickly destroyed this system by learning how muchthey needed to adjust the lines before releasing them to the public.

Sports Betting Algorithm Ideas

When this happened, a few smart sports bettors were able to find value in some of the games where the bookmakers moved the lines too far to compensate for the home underdog bias.

The value wasn’t available in every game, but in some games the line was moved too far, and there was value on the road favorite’s side instead of the home underdogs.

Baseball Betting Model

This dance between the bookmakers and smart sports bettors continues today, and will continue as long as there’s a sports betting market.