# Matching Team Names in Sports Betting Data: A Fuzzy Matching Approach

As a data engineer with a focus on predictive modeling for sports betting, one of the key challenges is matching team names from different data sources. In this blog post, we will explore how to use fuzzy matching to match team names from different sources and discuss an example implementation in Python. Additionally, we will introduce a new endpoint from BeatTheBookieDataService that provides a comprehensive matching of team names.

## Matching Team Names with Fuzzy Matching

The BeatTheBookie system relies on data from various sources such as football-data, understat, and fivethirtyeight. However, these sources may use slightly different team names, which can make it challenging to match them accurately. This is where fuzzy matching comes in handy.

Fuzzy matching is a string matching technique that allows us to find approximate matches between strings with varying degrees of similarity. It is particularly useful when dealing with minor differences in team names such as spelling variations, abbreviations, or different naming conventions.

Python provides a popular library called fuzzywuzzy that makes it easy to implement fuzzy matching in our data engineering pipeline. The library includes a function called `fuzz.ratio()` which calculates the Levenshtein distance between two strings, representing the similarity score as a percentage. We can set a threshold to determine how close the matches should be to consider them as valid matches.

Here’s an example implementation of fuzzy matching in Python:

```from fuzzywuzzy import fuzz
from fuzzywuzzy import process

def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2):
"""
:param df_1: the left table to join
:param df_2: the right table to join
:param key1: key column of the left table
:param key2: key column of the right table
:param threshold: how close the matches should be to return a match, based on Levenshtein distance
:param limit: the amount of matches that will get returned, these are sorted high to low
:return: dataframe with both keys and matches
"""
s = df_2[key2].tolist()

m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit))
df_1['matches'] = m

m2 = df_1['matches'].apply(lambda x: ', '.join([i[0] for i in x if i[1] >= threshold]))
df_1['matches'] = m2

return df_1

```

In this code, `df_1` and `df_2` are the two dataframes to be matched, `key1` and `key2` are the key columns in `df_1` and `df_2` respectively, and `threshold` is the similarity threshold for valid matches. The `limit` parameter specifies the maximum number of matches to return, sorted by similarity score.

## Identifying Unmatched Teams

Although fuzzy matching can be effective in matching team names, it may not be able to match all teams accurately. Some team names may have low similarity scores or may not have any matches in the given threshold and limit. It is important to identify these unmatched teams for further investigation.

```# Example usage of fuzzy_merge
df_result = fuzzy_merge(df_base, df_transfermarkt_unmatched, 'team', 'team_name', threshold=90, limit=2)

# Identify unmatched teams
df_unmatched = pd.concat([df_result['matches'], df_transfermarkt_unmatched['team_name']]).drop_duplicates(keep=False

```

The `df_unmatched` dataframe is created by concatenating the ‘matches’ column from `df_result` with the ‘team_name’ column from the original `df_2` dataframe and dropping duplicates. This results in a dataframe that contains the team names from `df_2` that were not matched by the fuzzy matching algorithm. These unmatched teams can be manually checked and corrected if necessary to ensure the accuracy of the matched dataset.

## BeatTheBookieDataService’s New Endpoint

As a data service provider catering to sports betting, BeatTheBookieDataService offers a new endpoint that provides a comprehensive team name matching. This endpoint, which can be accessed at https://beatthebookie.blog/beatthebookie-data-service-endpoints/, contains the entire matching process implemented in the system, eliminating the need for manual matching and ensuring the accuracy and consistency of team names across different data sources.

## Conclusion

In conclusion, fuzzy matching is a powerful tool for data engineers in the sports betting industry to match team names from different data sources. By using fuzzywuzzy in Python, data engineers can overcome minor differences in team names and improve the accuracy of their predictive models.