5/11 Blog

 I've had a cold and a sore throat for the last few days. I think it's because I just came to Shanghai and haven't adapted yet.

To help explain the importance of pricing the soccer matches you want to bet on, we'll use a simple example to explain how to do so. It is important to note that this pricing method actually has a lot of holes in it and on its own will not help you find value in the soccer betting market.

First, I used a Poisson model to create 1X2 odds for the final round of matches in the Premier League 2020/21 season. By using Infogol's expected goals scored data for that season, we can calculate the offensive strength and defensive strength of each team at home and on the road.

By using the ratio of team averages to league averages, we can evaluate the team's ability to score and concede goals relatively. We do not use actual goals scored, but rather expected goals scored. This data provides a more accurate picture of team performance and reduces the impact of randomness and luck on the 38-game season.

Home Attacking Strength (HAS) = team's expected goals per home game (xG) / league team's average expected goals per home game (xG) (higher is better)

Home Defensive Strength (HDS) = Expected goals conceded per home game (xGA) / Average expected goals conceded per home game (xGA) for league teams (lower is better).

Away Attacking Strength (AAS) = team's expected goals per away game (xG) / league team's average expected goals per away game (xG) (higher is better).

Away Defensive Strength (ADS) = Expected goals conceded by the team per away game (xGA) / Average expected goals conceded by the league team per away game (xGA) (the lower the better).

Next, we need to break these down to the specific game we want to price. We can then use the HAS for the home team and the ADS for the away team to calculate the expected number of goals scored by both teams.

Iwill demonstrate this process using the match between West Ham and Southampton in the 38th match week of the EPL 2020/21 season as an example

West Ham goals

West Ham HAS x Southampton ADS x League teams' average xG per home game

0.97 x 1.20 x 1.54 = 1.792

Southampton Goals

Southampton AAS x West Ham HDS x League team average xG per away game

0.88 x 1.04 x 1.37 = 1.253

Therefore, when the two teams play at the London Stadium, West Ham is expected to score 1.792 goals and Southampton is expected to score 1.253 goals. However, the actual score is unlikely to be 1.792 to 1.253, so we need to find the probability distribution across a range of outcomes.

We can use the Poisson function in Excel to calculate the probability distribution of the different number of goals scored by each team in the match (in this example, we use data ranging from 0 to 5). Using the example above, the probability distribution is calculated as follows:

Teams         0 goals     1 goal     2 goals     3 goals     4  goals     5 goals

West Ham   0.167        0.299     0.268        0.160       0.072         0.026

Southampton 0.286     0.358     0.224         0.094       0.029         0.007

Based on this, we can calculate the probability of a single score. For example, to calculate the probability of a 0-0 tie:

0.167 x 0.286 = 0.0477 = 4.77%

We can use this technique to calculate the total probability of a West Ham win, a draw and a Southampton win. Below is the probability of each possible score between the two teams, calculated from our Poisson distribution:

Result Probability (%)                 Odds

West Ham win 49.32%                2.03

Draw 23.72%                               4.22

Southampton win 26.96%            3.71

However, Pinnacle's actual closing odds of 2.17(Original is 2,03) on a West Ham win for this match suggest that they may have been underestimated in this match.

This is so fun! 

Total Hr:6