How sports stats are collected

Fri, Mar 7, 2025
by CapperTek

Sports statistics collection has come a long way since the old days of manual, paper-based records. Today, a whole host of digital tracking systems and sophisticated technology has been integrated. This is critical for betting contours to be able to offer the odds that they do and still come out on top, but it’s much bigger than that. Coaches use them in their strategies, they are great for entertainment, and even umpires need them. Today, we are going to take a deep dive into the ways sports statistics are collected.


It is these extensive technologies which allow for:

Traditional data collection methods

Before powerful computers came about, stats were collected manually by official scorers, statisticians, and team analysts. Key events like goals, assists, fouls, possession changes, and player substitutions. This was then compiled into detailed match reports that teams, leagues, and analysts would review post-game. 


Baseball introduced scorecards where statisticians manually recorded: 

  • hits

  • runs

  • strikeouts

  • errors


In basketball, on standardized sheets, these same specialists logged:

  • points

  • assists

  • fouls


For cricket, detailed ball-by-ball scoring logs were used to document every play. Referees and umpires enforced rules while tracking infractions, timekeeping, and key game decisions. These records were subject to human error though unfortunately, and didn’t go into an incredible degree of depth.

How ordinary stats are collected nowadays

The common principle of today’s stats is processing information and analyzing it for performance insights, broadcasts, fantasy sports, and betting. Despite this, official scorekeepers are still tasked with manually recording many of the same phenomena. These information bits get fed into the computer system which displays events on the scoreboards and factor into things like batting averages and free throw percentages.




On-field equipment


Beyond that, there is an extensive array of equipment used to track lots of different stats to dazzle audiences and TV viewers. These include high-speed cameras and radars to measure ball speed, launch angle, and player movements. Cameras are positioned around soccer and football stadiums, GPS trackers follow players, and RFID chips are embedded in balls themselves to help measure:

  • distances covered

  • player movements

  • ball placement

  • running speeds.


In cricket, ball-tracking technologies record pitch movements, batting strike rates, and bowling speeds, ensuring accuracy in umpiring decisions and performance analysis. 

Machine learning

Machine learning plays a crucial role in the processing and interpreting of vast amounts of collected sports data. By learning from past data, models are able to predict future events, evaluate player performances, and identify trends that may not be immediately obvious. 


In football, for example, machine learning algorithms analyze years of player statistics to predict the likelihood of a touchdown or the success of a pass. These are based on variables such as player speed, defensive positioning, and historical play patterns. In baseball, models assess the likelihood of a hit based on the pitch type, batter’s performance history, and even weather conditions.


Coaches take advantage of machine learning to prevent player injuries. By analyzing data from wearables and things like heart rate monitors, artificial intelligence models can help spot patterns of fatigue or stress, which in turn coaches take into account to help them adjust player workloads.


Here are some examples of equipment enriching machine learning data:


  • Hawk-Eye: initially developed for tennis and cricket, uses a network of cameras and AI to provide live match analysis, track ball trajectories, and help with officiating decisions. Its precision is also used in football for goal-line technology.

  • Statcast: used primarily in Major League Baseball, it combines computer vision and machine learning to capture every play, including player positioning, ball velocity, launch angle, and more. This helps inform announcer commentary.

  • Second spectrum: every movement on the basketball court is mapped, tracking players, ball possession, and game strategies. 

Influence of sports betting

The rise of sports betting has made a huge impact on the impetus to collect large numbers of stats. In order for a betting contour to be competitive, it needs to have lots of statistics. Interestingly, the majority of bets people place aren’t on the biggest national leagues that everybody watches on TV. 


Instead, due to the abundance of local and regional leagues, most bets are based on lower-profile games. This creates a lot of pressure to attain sufficient data in vast quantities and also make it available as quickly as possible so bettors don’t have to wait. This has necessitated the advent of a whole new industry – data providers. 



Data providers


Due to the vast quantities of data that have to be collected, processed, and analyzed, these specialized companies have a challenging task. They first have to obtain the information from the scorers, process it, check information multiple times in automated fashion to make sure it's legit, and then update all of that in real time. This is precisely what gave to the rise of prop bets.


Nowadays, people like placing small bets based on in-game situations. In order for gambling outfits to provide this opportunity, they have to make sure to provide a fair deal to both sides of bets while ensuring a profit for themselves. That requires very sharp data tracking and that information has to be quickly verified and published for betting.


These statistics commonly include:

  • who kicks the next goal

  • the over and under for total yards gained

  • the number of completions by a quarterback

  • whether a pitcher strikes out a particular batter

  • total runs scored by a team


Big data and cloud computing

These types of operations are quite resource-intensive. Advanced technologies must be used to manage and make sense of it. Cloud computing and big data tools provide the infrastructure necessary to accommodate that. Technologies such as Hadoop and Spark are used to break down large datasets into manageable chunks, enabling rapid processing and real-time analysis.


Cloud computing allows for big-time processing to be completed even if an organization does not have sufficient computing power in-house or simply would prefer to outsource that responsibility. Buying that kind of infrastructure on their own would cost data providers a ton. 


Popular cloud computing platforms are:

  • Amazon Web Services

  • Microsoft Azure

  • Google Cloud


These clouds also provide advanced security with encryption and access controls to safeguard sensitive player data and proprietary analytics. On top of that, subscribers get robust recovery and backup solutions that secure data in the event of a system failure.

Fantasy sports

In these types of leagues, participants build virtual teams based on real-live players’ performances. This has become a multi-billion-dollar industry. Users draft players, set lineups, and make trades based on player statistics. The success of fantasy sports platforms hinges on the collection and distribution of live sports data.


Popular platforms include:

  • ESPN Fantasy

  • DraftKings

  • Fanduel


Data companies deliver real-time updates instantly which are reflected in fantasy players’ points. This allows users to adjust their teams immediately based on live performance. The reason fantasy football is worth so much revenue is that users are charged fees to be able to participate in paid leagues and contests. The winner of the contests ends up winning a certain amount of money while the platform takes a commission for itself.