Ordered Logit Regression in Python

A statistical model used to predict the probability of multiple ordered outcomes, like home win, draw, or away win in a soccer match.

The coefficient in an Ordered Logit Regression that represents the impact of the team value ratio on the probability of each outcome.

The values in an Ordered Logit Regression that define the boundaries between the different possible outcomes.

The statistical significance of a coefficient in a regression analysis, indicated by a low p-value.

A measure of the variability or uncertainty of a coefficient.

The probability of observing the coefficient value if there was no real relationship between the variable and the outcome.

The advantage that a team playing at home has due to factors like familiar surroundings and crowd support.

A statistical technique where the dependent variable is continuous and the independent variables can be both continuous and categorical.

A statistical measure used to assess the accuracy of probabilistic predictions. A lower Brier Score indicates better prediction accuracy.

The percentage of successful predictions made by a model or a bookmaker. In this context, it refers to the accuracy of predicting football match outcomes.

A statistical method used to model football match outcomes using the ratio of home team and away team's values (Tn). The model attempts to predict the probability of different outcomes (home win, draw, away win).

The practice of adjusting betting odds by multiplying the odds for each outcome so that the total probability of all outcomes is greater than 100%. This allows bookmakers to make a profit regardless of the match result.

A measure of how close two models or sets of predictions are to each other. In this case, it compares the accuracy of the Parral model against the predictions of bookmakers.

An approach to estimating the accuracy of a model by considering how much the prediction would change if minor adjustments were made to the predicted probabilities.

The possibility of using the Parral model to make profitable bets in football matches.

In the context of this example, it refers to the probability of a draw occurring in a football match as estimated by a model. The model assigns a numerical value (percentage) representing its prediction for the likelihood of a draw.

In the context of football analysis, this term refers to a prediction made by a statistical model about the outcome of a match. Specifically, it refers to the probability of a team winning the match away from their home stadium.

This refers to the prediction made by a statistical model about the outcome of a match. Specifically, it represents the probability of the home team winning the match.

This term encapsulates the concept of a statistical model making a prediction about the outcome of a match. The model assigns a numerical value representing its prediction for the likelihood of a specific outcome, such as a win, draw, or loss. In the context of football, it's often expressed as a percentage.

When applied to football predictions, this term represents the actual outcome of a match. It records the winner of the match, denoted by 'H' (home win), 'D' (draw), or 'A' (away win).

In the context of football predictions, this term refers to the prediction made by a model, or in this case, derived from ordered logic, that corresponds with the highest probability outcome. This is the model's best guess, the outcome it considers most likely.

In the context of evaluating model accuracy, this refers to comparing a model's predicted outcome to the actual outcome observed in a real-world scenario, such as a football match. If the model's prediction matches the actual result, it's considered a 'correct' prediction.

A statistical measure used to evaluate the accuracy of a model's predictions. It is calculated by dividing the number of correct predictions by the total number of predictions made. In the context of predicting football matches, it represents the percentage of games where the model correctly predicted the outcome.