This blog came about as a reaction to a Twitter thread (see below) started by @FC_rstats. One of the ideas brought up in that thread, and the discussion that followed (between Sam Gregory and Koen Vossen) was the creation of an (open-source) approach to Player ID matching across multiple data providers.

The initial intention of this blog was to discuss an implementation of a Player ID matching system (PMS) that I designed a couple of years ago. It was my hope this could help inspire the creation of an open-source approach for Player ID matching. While writing this blog I had some new ideas on how to improve my approach. So, as a result, this blog has become a hybrid of implementation ideas and actual implemented design.

To build a robust PMS we need the player name (preferably first name & last name) or nickname (ie. Hulk or Ronaldinho) and date of birth (although this is not strictly necessary). Because the ultimate goal is to do as little manual work as possible, and to improve the robustness of the PMS, it’s important to start by building systems for matching Team IDs (TMS), Game IDs (GMS) and Competition IDs (CMS) across datasets. These systems will help decrease the search space per player from several thousands to between 1 and 30 at most.

We’ll first go over the basics of string similarity matching using Cosine Similarity (Wiki). Then I will discuss some design ideas for the CMS, GMS and TMS, followed by an explanation of the PMS.

Basics of Similarity Matching

To do any kind of similarity matching we’re going to make use of an approximate string matching technique. I propose the use of the Cosine Similarity to measure the similarity between sets of words (ie. the player or team names). I’ve also experimented briefly with a fuzzy matching algorithm FuzzyWuzzy, but I found the cosine similarity to give better results.

To properly use cosine_similarity() - part of sklearn.metrics.pairwise - and to ensure better matching, we’re going to standardize our set of names by:

  1. Removing any accents and using only letters from the Roman/Latin alphabet
  2. Removing any non-alphanumeric characters, like dashes, using regex
  3. Removing double spaces using regex
  4. Lower case all characters

Then, we’ll initialize a Term Frequency–Inverse Document Frequency (or TF-IDF for short) vectorizer and fit it to the complete set of names (for example all player names, or all team names). These TF-IDFs are normally used to describe amounts of text greater than two or three words so, we’ll initialize TfidfVectorizer() with a custom analyzer that will split the names up into n letter “words” or sub-strings. This will ensure we increase the “surface area” of our matching algorithm, by giving it more to compare to. Fitting a is done to assign frequently occuring parts of names with a lower weight than more unique parts of names.

In my implementation n = 3, thus turning names into 3 letter sub-strings.

For example, when fitting the vectorizor on team names this means that “fc” will have a lower importance (due to its relatively high occurance frequency within a dataset of team names) compared to a “liv”, “erp” and “ool” or “bar”, “cel” and “ona” - the 3 letter ngrams from Liverpool and Barcelona respectively.

def ngrams(string, n=3):
	Chop names into 3 letter 'words' to try and make better fits
	string = re.sub(r'[,-./]|\s', r'', str(string))
	ngrams = zip(*[string[i:] for i in range(n)])
	return [''.join(ngram) for ngram in ngrams]
# initialize the vectorizer with a function ngrams that splits data into words of 3 letters
vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams)  
# fit on the whole dataset of standardized names available 

After fitting this vectorizer on the whole standardized name dataset, we use the fitted vectorizer to transform a subset of standardized names (we’ll discuss what these subsets are later) and finally we’ll get our cosine similarity matrix for that subset.

# transform subset using vectorizer fit to whole dataset
tf_idf_subset_matrix = vectorizer.transform(subset)
# get cosine similarity vector
cosine_sim_matrix = cosine_similarity(tf_idf_subset_matrix, tf_idf_subset_matrix)

Matching Competitions, Teams and Games

With this bit of similarity matching information out of the way we can start building our matching system. The matching system consists of four parts (competition, team, game and player). We’ll build these parts in this order, because each new system depends on the previous system.

1. Competition Matching

We start by matching competition names across datasets, depending on the amount of competitions available. This can be done either:

  • Manually (with just a couple competitions it’s overkill to build an algorithm for it)
  • Semi-automatically, here we automatically match on a cosine similarity greater than a certain threshold and automatically disregard all matching options when the maximum cosine similarity doesn’t go over a certain threshold. This threshold depends on the n in the ngrams function, because a lower n leads to higher cosine similarity scores.

It’s obviously important to have an accurate set of matches on the top level (in this case competitions). This means we should probably keep the automatic matching threshold low, or just do this step manually once or twice every season.

1.1 Season Matching

To get the correct amount of teams, and the correct team names per season we should also match the season IDs, but this can easily just be done by hand, or by some date range.

2. Team Matching

Now that we have matched the competitions and seasons we can use the cosine similarity approach for team matching. Here we have the added benefit of filtering by competition name and season. This gives us a significantly reduced search space, going from thousands of options to approximately 20 per competition per season. In turn this means we can decrease the similarity threshold for automatic matching. Or, and this is one of the ideas I came up with when writing this blog, we can try some other approach where we assign each team in one dataset a team in another dataset by using the Hungarian algorithm, maximizing the total cosine similarity in the cosine similarity matrix.

Below is some example code showing how to match two lists of Portuguese team names from the ‘21/22 season using the Hungarian algorithm (linear_sum_assignment in Scipy). These particular names come from and

from scipy.optimize import linear_sum_assignment
import numpy as np

ce_teams = ['porto', 'sporting', 'benfica', 'braga', 'guimaraes', 'gil vicente', 'santa clara', 'famalicao', 'boavista', 'pacos ferreira', 'maritimo', 'portimonense', 'moreirense', 'estoril', 'belenenses', 'vizela', 'tondela', 'arouca']
tm_teams = ['fc porto', 'sporting cp', 'sl benfica', 'sc braga', 'vitoria guimaraes sc', 'fc famalicao', 'boavista fc', 'portimonense sc', 'gd estoril praia', 'cd santa clara', 'gil vicente fc', 'fc pacos de ferreira', 'cd tondela', 'belenenses sad', 'moreirense fc', 'fc vizela', 'fc arouca', 'cs maritimo']

vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams)  # fit the vectorizer on all teams

tfidf_clubelo = vectorizer.transform(ce_teams)
tfidf_tm = vectorizer.transform(tm_teams)

cosine_sim_matrix = cosine_similarity(tfidf_clubelo, tfidf_tm)

row_idx, col_idx = linear_sum_assignment(

Here are the results in a cleaned up table, with the matched names side-by-side, and their associated cosine similarity values.

clubelo transfermarkt cosine_similarity
arouca fc arouca 0.86
belenenses belenenses sad 0.826
benfica sl benfica 0.805
boavista boavista fc 0.882
braga sc braga 0.719
estoril gd estoril praia 0.533
famalicao fc famalicao 0.847
gil vicente gil vicente fc 0.892
guimaraes vitoria guimaraes sc 0.678
maritimo cs maritimo 0.824
moreirense moreirense fc 0.86
pacos ferreira fc pacos de ferreira 0.65
portimonense portimonense sc 0.884
porto fc porto 0.658
santa clara cd santa clara 0.848
sporting sporting cp 0.717
tondela cd tondela 0.752
vizela fc vizela 0.84

The small caveat to this approach is matching team names across more than two datasets can be a bit more involved.

3. Game Matching

With the team names matched up it now becomes really easy to match fixtures. We can do this by creating a new Game ID (GMS ID) that is a combination of the Home Team TMS ID (the newly created team ID we assigned to every matched set of teams), the Away Team ID TMS ID and the timezone adjusted date of the match. If we do not have the timezone date, but only the date, it’s still possible to do this, if we work under the assumption that no two teams will play each other more than once per 48 hours.

Matching Players

Now that we’ve discussed the prerequisite systems that will help us reduce the search space, we can finally discuss the design of the actual Player Matching System. The main objective should be to leverage the TMS (and potentially the GMS) to drastically reduce the search space, increase accuracy, and decrease false positive matches for each player by creating a matching funnel. This matching funnel will match players by a set of rules decreasing in strictness.

Using the Hungarian algorithm in the PMS was not in my initial design (it wasn’t either for TMS), and because of possible differences in number of players given per data source (ie. some source might have 32 players for a squad whereas others might only have 25 for the same team) I don’t see a straightforward way to use it in the PMS.

Before describing how we’re going to leverage everything discussed before to build our matching funnel we need to take note of two potential data issues, and one idea I have not implemented in my design that might prove helpful.

Wrong Birthdays

One important issue, that is easy to overlook in a name matching system, is wrong birthdays. From debugging my own implementation I’ve noticed errors in birthdays can be simple typos, a translation error from MM-DD-YY to DD-MM-YY or they might just be a day of, for whatever reason.

This means that we will need to handle cases where birthdays are approximately correct by specifying a date of birth range. From some trial and error I’ve found that adding the following dates to the date of birth range will help improve matching:

  • The original birth date
  • The date with +1 or -1 for the month, but within the same year
  • The date within +10 and -10 for the day, but within the same month
  • The inverted date format if it exists (MM-DD-YY, if all our dates are assumed to be DD-MM-YY)

We will not adjust the year, for fear of matching too many false positives.


The second issue worth discussing is how every data provider uses their own judgement for using nicknames, full names or a combination of both. (It’s highly advised to add both nickname and full name to the matching system when both are provided).

In my initial implementation this issue was resolved by using a Python package called gsearch which allowed Google searches via Python. This would have allowed search queries like f"{player_name} (footballer) wiki (date_of_birth)". This google search would almost always - 99% of the time, even for obscure players - give us the Wikipedia article for the player. Using the Wikidata Q-code of the article - which we could obtain with a simple web request - could then match up search queries like “Ronaldo de Assis Moreira (footballer) wiki 03-21-1980” and “Ronaldinho (footballer) wiki 03-21-1980”, because they both link to the same Wikidata page with Q-code Q39444.

All in all, this seems quite cumbersome - and it probably was. While trying to replicate this for this blog I found that the gsearch package doesn’t quite work anymore. To get similar results you’d need to get a paid Google Search API Key.

As an aside, you can find the Wikidata page for any Wikipedia article in the left menu on Wikipedia under “Tools”.

Luckily, there is an easier and faster way now that either wasn’t available, or I probably just didn’t find it back then, using a combination of search and page from the Wikipedia Python package. This combination will give us a Wikipedia page’s pageid that we can use to identify identical players that came about from different search terms.

Unfortunately, I have not done thorough testing on this and simply grabbing only the first result might not work for more obscure players.

Below is some sample code to find the pageid for Ronaldinho, Hulk and Yago Pikachu. It works even with wrong date of birth values.

import wikipedia

def search_footballer(player_name, date_of_birth):
    query = f"{player_name} (footballer) {date_of_birth}"
    term =, results=1, suggestion=False)[0]
    page =, preload=False)  # set preload to False to improve speed
    return page.pageid

search_footballer(player_name="Ronaldinho", date_of_birth="03-21-1980")
search_footballer(player_name="Ronaldo de Assis Moreira", date_of_birth="03-21-1980")

search_footballer(player_name="Hulk", date_of_birth="07-25-1986")
search_footballer(player_name="Givanildo Vieira de Sousa", date_of_birth="07-24-1986")  # wrong date of birth

search_footballer(player_name="Yago Pikachu", date_of_birth="06-05-1992")
search_footballer(player_name="Glaybson Yago Souza Lisboa", date_of_birth="05-06-1992")  # wrong date of birth

It’s safe to say that finding nickname and regular name matches this way can be time consuming (it takes about 700ms to get a result back from Wikipedia with the above code). This approach should probably only be used when we have players in the same team with the same data of birth (range) that are no simple match.


An idea that I have not implemented is using hypocoristics. Hypocoristics are - and this comes straight from Google - “a pet name, nickname, or term of endearment — often a shortened form of a word or name”. For example, Micheal might be called Mike or Robert might be called Bob. This happens in a lot of languages and our PMS should probably incorporate this. For now, I’ve only compiled lists (coming mostly from Wikipedia) of hypocoristics in English, Spanish and Portuguese (see .txt files on GitHub), though I have no way of knowing if they are actually correct and/or useful.

The Funnel

Using combinations of the ideas discussed above we can construct a search funnel that will help us match players to one another with minimal risk of false positives.

Below is a list of some of the options we have for matching player names. I’ve also highlighted some potential matching mechanisms that might more easily result in false positive matches, because we use too much incomplete information.

  1. Match on exact name, exact date of birth and TMS ID of a team this player played for in both datasets.
  2. Match on exact name & exact date of birth (maybe we don’t have data from the same seasons for a particular player)
    • This can introduce some errors when two different players exists with same name and date of birth
  3. Match on exact name & TMS ID when no date of birth is available
    • This can introduce some errors when two players with the same name on the same team have no date of birth, but we can try to resolve this using an exclusive GMS search.
  4. Match on approximate name (cosine similarity above certain threshold), exact date of birth and TMS ID
  5. Match on approximate name (cosine similarity above certain threshold - higher than in point 4), date of birth range and TMS ID
  6. Match on approximate name (cosine similarity above certain threshold - higher than in point 5) and date of birth range
    • This can introduce some errors when two players exists with same name and date of birth
  7. Match on approximate name (cosine similarity above certain threshold - higher than in point 6) and TMS ID
  8. Match on Wikipedia search for name and date of birth when two players with same TMS ID have the same date of birth range
    • This can introduce some errors because we are “blindly” following the Wikipedia API results


It’s safe to say that building the actual code capable of doing all of this in a correct, effective and efficient way - all while juggling different sets of matched and unmatched player IDs, new incoming providers and new IDs - is a huge undertaking. I can only hope this blog has provided some clarity, some ideas and some insights into the design of a Player ID Matching System.

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