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Expected Threat (xT) |
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The expected threat or xT model is a possession-based model. That is, it |
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divides matches into possessions, which are periods of the game where the same |
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team has control of the ball. The key insights underlying xT are that (1) |
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players perform actions with the intention to increase their team's chance of |
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scoring, and (2) the chance of scoring can be adequately captured by only |
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considering the location of the ball. |
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Point (2) means that xT represents a game state solely by using the current |
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location of the ball. Therefore, xT overlays a :math:`M \times N` grid on the |
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pitch in order to divide it into zones. Each zone :math:`z` is then assigned |
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a value :math:`xT(z)` that reflects how threatening teams are at that location, |
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in terms of scoring. These xT values are illustrated in the figure below. |
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.. image:: default_xt_grid.png |
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:width: 600 |
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:align: center |
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The value of each zone can be learned with a Markov decision process. The |
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corresponding code is shown below. For an intuitive explanation of how this |
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works, we refer to `Karun's blog post <https://karun.in/blog/expected-threat.html>`_. |
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.. code-block:: python |
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import pandas as pd |
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from socceraction.data.statsbomb import StatsBombLoader |
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import socceraction.spadl as spadl |
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import socceraction.xthreat as xthreat |
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# 1. Load a set of actions to train the model on |
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SBL = StatsBombLoader() |
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df_games = SBL.games(competition_id=43, season_id=3) |
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dataset = [ |
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{ |
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**game, |
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'actions': spadl.statsbomb.convert_to_actions( |
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events=SBL.events(game['game_id']), |
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home_team_id=game['home_team_id'] |
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) |
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} |
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for game in df_games.to_dict(orient='records') |
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] |
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# 2. Convert direction of play + add names |
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df_actions_ltr = pd.concat([ |
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spadl.play_left_to_right(game['actions'], game['home_team_id']) |
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for game in dataset |
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]) |
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df_actions_ltr = spadl.add_names(df_actions_ltr) |
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# 3. Train xT model with 16 x 12 grid |
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xTModel = xthreat.ExpectedThreat(l=16, w=12) |
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xTModel.fit(df_actions_ltr) |
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# 4. Rate ball-progressing actions |
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# xT should only be used to value actions that move the ball |
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# and that keep the current team in possession of the ball |
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df_mov_actions = xthreat.get_successful_move_actions(df_actions_ltr) |
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df_mov_actions["xT_value"] = xTModel.rate(df_mov_actions) |
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.. seealso:: |
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This `notebook`__ gives an example of the complete pipeline to train and |
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apply an xT model. |
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__ https://github.com/ML-KULeuven/socceraction/blob/master/public-notebooks/EXTRA-run-xT.ipynb |
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