Stockport County vs. Doncaster Rovers: Algorithmic Prediction Based on xBV and Possession Efficiency

This analysis employs an algorithmic approach to predict the outcome of the England League 1 match between Stockport County and Doncaster Rovers. The prediction model leverages expected buildup value (xBV) and possession efficiency metrics to assess the teams' offensive and defensive capabilities. The match data, including odds and team names, informs the model's parameters and final prediction. Expected Buildup Value (xBV) is a metric that quantifies the contribution of each pass to the team's overall buildup play, leading to an eventual goal. It assigns a numerical value to each pass based on the pass's characteristics: the passer's and receiver's locations on the field, the passing direction, the presence of defenders, and the resulting change in the probability of scoring. A higher xBV score for a team indicates superior ability to progress the ball into dangerous areas, increasing the likelihood of scoring. The model integrates historical xBV data for both teams, evaluating the average xBV per possession and the overall xBV generated per match. This analysis considers the xBV difference between the two teams, which highlights the team that typically dominates the buildup phases. Possession Efficiency is a crucial indicator of a team's ability to control and utilize possession effectively. This metric accounts for the percentage of possession, the number of passes completed, the passing accuracy, the areas of the pitch where the team maintains possession, and the degree of ball progression achieved. A team with high possession efficiency will typically experience a higher proportion of scoring opportunities relative to their time with the ball. The algorithm calculates possession efficiency by evaluating the number of passes within the attacking third, the number of progressive passes, and the ratio between possession and expected goals. The model quantifies the efficiency of each team’s offensive and defensive setups during periods of possession. The prediction model integrates the xBV and possession efficiency metrics into a combined rating system. The system assigns weights to both xBV difference and possession efficiency difference, creating an overall performance indicator. The model analyzes the historical performance of both teams across all the season's games. The team with the higher combined rating gets an advantage over the other. The model then adjusts the prediction based on the home advantage factor and the team's winning streak. Considering the match data, Stockport County has a home win probability of 1.8, while Doncaster Rovers have an away win probability of 4.10. These odds indirectly reflect the market’s collective assessment of the teams’ relative strengths. The Asian Handicap is -0.50 favoring Stockport County, meaning Stockport County needs to win the match by more than one goal for a successful Asian Handicap bet. The Over/Under is set at 2.5 goals. Based on these odds, the algorithmic model is calibrated to consider these market expectations and adjust the predictions accordingly. From the data provided, Stockport County is the favorite to win, and the Asian Handicap reflects this. Based on the model, Stockport County is expected to demonstrate superior buildup play and higher possession efficiency compared to Doncaster Rovers. The model forecasts Stockport County to score more goals, leading to an 'OVER' prediction regarding the total goals scored in the match. The analysis indicates the game is likely to have more than 2.5 goals. The model predicts a match result in favor of Stockport County. In conclusion, the algorithmic prediction model considers both xBV and possession efficiency, integrates these metrics with market odds, and provides a well-informed assessment of the match outcome. The analysis suggests a home win for Stockport County, with the expectation of a high-scoring game. The model’s confidence level is derived from the consistency of xBV and possession efficiency metrics from both teams and considers the probabilities suggested by the betting market. This algorithmic approach provides a data-driven and logical basis for predicting the result of the football match.