Python for Sports Odds Compiling

Based on the original Principles of Sports Odds Compiling Course, this Python version covers all the same topics, only this time in code! Probability distributions, data manipulation, and modeling in Python to complement your odds making skills.

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Course curriculum

1. Introduction and setup
2. All Jupyter Notebooks Required for the Course
3. All Excel materials required for the course
1. Part 1 - variables, data types, implied probabilties
2. Part 2 - Boolean expressions, complement, addition, multiplication rules
3. Part 3 - SQL. Introducing DuckDB, SELECT, FROM, VALUES, SUM
1. Part 1 - Functions, parameters, overlapping events, data frames
2. Part 2 - Normalisation, applying the addition rule
3. Part 3 - Boolean masks
4. Part 4 - Combining boolean masks (Python's version of IF AND, IF OR)
5. Part 5 - SQL. CASE WHEN, THEN, aliasing with AS
6. Part 6 - SQL. WHERE
1. Part 1 - Lists, list comprehensions, SUM(), zip function
2. Part 2 - Custom overround functions
3. Part 3 - Overround redistribution, custom function
4. Part 4 - SQL. VALUES
1. Part 1 - Math.comb, custom binomial function
2. Part 2 - Looping for a PMF, dictionaries, APPPEND
3. Part 3 - binomial function for goal in next n minutes
4. Part 4 - SQL. GROUP BY, WHERE filters, UNION ALL
1. Part 1 - Custom Negative Binomial Function
2. Part 2 - Range() to iterate through trials, appending dictionaries, .to_string
3. Part 3 - Filtering to sum probabilities
4. Part 4 - SQL. Tables of trial counts, ORDER BY, filtering with WHERE
1. Part 1 - Scipy, built in Poisson function, looping for a PMF
2. Part 2 - Read_CSV, XLSX, usecols and column renaming
3. Part 3 - Data analysis, MEAN, LEN
4. Part 4 - Observed v Expected distribution
1. Part 1 - scipy, nested loops to build a grid, zeroes function
2. Part 2 - Score grid derived markets
3. Part 3 - SQL. Creating a grid in SQL
4. Part 4 - SQL. Loading dataframes, GROUP BY, COUNT, AVG
1. Part 1 - NORM cdf, mean, standard deviation, variance
2. Part 2 - looping the cdf, list(range)
3. Part 3 - Norm.ppf: the inverse function
1. Part 1 - Apply and lambda functions
2. Part 2 - Two ball betting, Iterrows, if..elif...else
3. Part 3 - SQL. Querying the dataframe with CASE WHEN, CROSS JOIN
1. Part 1 - Survival model function and drawbacks
2. Part 2 - Writing a linear function, max() and min() to clip outputs
3. Part 3 - Adjustment by half, minute
4. Part 4 - Time of the first goal : a better solution
5. Part 5 - Comparing linear v exponential outputs
6. Part 6 - SQL. Extracting minute data for goal based markets
1. Part 1 - Bradley Terry Function, ratings dictionary, filling a list with ratings and probabilities
2. Part 2 - Simulating a match and tournament
3. Part 3 - SQL. CASE WHEN, AVG, WHERE OR and ABS
1. Part 1 - The ELO logistic function
2. Part 2 - ELO Updating function
3. Part 3 - Updating an ELO ratings dataset with the function and iterrows
1. Part 1 - Variables, load data function, merging historic and future datasets
2. Part 2 - Building the functions
3. Part 3 - Building and running the loop to update ratings
4. Part 4 - Rankings and Ratings
5. Part 5 - What's next?

About this course

£795.00
55 lessons
11.5 hours of video content

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Python for Sports Odds Compiling

Course curriculum

Python for Sports Odds Compiling - Introduction

L01 Axioms and Probability Questions

L02 Addition Rule in depth

L03 Overround

L04 Binomial Distribution

L05 Negative Binomial Distribution

L06 Poisson Distribution

L07 Correct score grid

L08 Normal distribution

L09 Normal Distribution Exercise

L10 Alternative to Exponential Distribution

L11 Ratings systems : Bradley Terry

L12 Ratings systems : ELO

L13 Building an NBA Ratings Predictive Model

About this course

Discover your potential, starting today