Creator: American Soccer Analysis, Method: Gradient-boosted trees, Window: Two possessions, Off-ball information: Possession history proxies 

University of Michigan - Ann Arbor

Its common for good teams to control the ball for an entire game but never score a goal. Possession models look to further quantify what kinds of possessions are actually valuable. A simple version is location-based possession value which tells you the probability of scoring based on where the ball is at that point in time. As you get closer to goal that probability increases.

https://fivethirtyeight.com/features/possession-is-the-puzzle-of-soccer-analytics-these-models-are-trying-to-solve-it/

Soccer possession models

Creator: S. Rudd
Method: Markov chain
Window: One possession
Off-ball information: Defensive states tagged in event data

Markov Chains

Creator: N. Mackay, Method: Logistic regression and GAM, Window: One possession

Possession-Based Model

Creator: K. Singh, Method: Markov-line, Window: Next 5 actions

Expected Threat (xT)

Creator: KU Leuven DTAI, Method: Gradient-boosted trees, Window: Next 10 actions, Off-ball information: Possession history proxies

Valuing Actions by Estimating Probabilities (VAEP)

Creator: J. Fernandez, Window: Next goal, Off-ball information: Full tracking data

Expected Possession Value (EPV)

Creator: Stats Perform, Method: Gradient-boosted trees, Window: Next 10 seconds, Off-ball information: Possession history proxies

Possession Value (PV)

Goals Added (g+)

Creator: StatsBomb, Method: Gradient-boosted trees, Window: Two possessions, Off-ball information: Broadcast freeze frame

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