The Deutsche Football League, along with Amazon Web Services, announced two new match statistics for upcoming Bundesliga games. The stats join the league’s Bundesliga Match Facts portfolio. This gives television viewers incredible insight in specialized area of play.
Introduced in 2020, Bundesliga Match Facts provides innovative statistics in essentially real-time speed. The software collects 3.6 million data points and simultaneously analyzes them. These figures include expected goals, shot efficiency, average position, speed alert, attacking zones, most pressed player, passing profile, set piece threat and skill.
Now, the Bundesliga introduces pressure handling and win probability to its The two new statistics that are being introduced are pressure handling and win probability.
Bundesliga’s two new, in-game statistics
Pressure handling tracks every player on the pitch and determines who handles pressure the best.
Performing under pressure is a key attribute that can make or break an entire match. The software measures predicts this figure by using escape rate. Escape rate tracks how often a player can break out of high-pressure situations. That can be dribbling out, finding a teammate, taking a shot or drawing a foul.
Win probability attempts to predict which team will win the match. The measurement can take into account notable swings in every moment of each game. Factors such as score and time remaining in the game are obviously main components of the probability. This statistic will also use other aforementioned Bundesliga Match Facts to determine the win rate.
These innovative data analytics help give fans a deeper insight to how exactly each match is going. Along with predicting outcomes, they can also allow a better understanding to each team’s strategy and how each specific player is performing.
Pressure handling and win probability will be introduced on Friday. Bayern Munich hosts Bayer Leverkusen at 2:30 (ET) on ESPN+. There will then be six games on Saturday and two on Sunday. ESPN+ currently carries all Bundesliga matches in the United States.
PHOTO: DFL on YouTube
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Chris
September 30, 2022 at 2:33 pm
I’m still trying to figure out what “expected goals” are. “Expected” by whom or what?
dave
September 30, 2022 at 3:21 pm
Two calculations for xG are commonly modeled:
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* Shot-based xG – accounts for where on the field shots are taken from
* Non-shot xG – accounts for where the ball is possessed and passed, regardless of whether it turns in to a shot
* If a source does not specify type of xG, it is almost always shot-based
.
With very large volumes of data, machine learning models can be trained to make fairly reliable estimates of xG for every situation. These can be aggregated to levels such as sum by team over the course of a game. The metric is also useful for purposes such as assessing keepers – shot-based xG minus goals allowed is roughly “goals prevented”
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If curious, 538 use xG as a key component of league soccer predictions. Their methodology section explains xG and why it can be more predictive than final score. Goals scored by a team in a soccer game roughly follow a Poisson distribution, with lambda roughly sum of xG