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When the Oakland Athletics, a Major League Baseball team, won 20 games in a row in 2002, the professional ____ industry was shocked.

The Oakland Athletics ____ a small team with limited funds, which made it difficult for them to attract elite players.

____ did they do it?

The key to their success was adopting sabermetrics, a statistical ____ used for making decisions in baseball.

Sabermetrics was created in the late ____

It tries to use large amounts of information to discover patterns, trends, and insights that ____ difficult to find with traditional ways of studying statistics.

Normally, coaches and managers prefer players who can hit the ball hard and far, for such players tend to hit ____ home runs and extra-base hits.

Sabermetrics suggests that players’ offensive skills can be better measured ____ the frequency with which they safely reach base.

It does not ____ whether this is achieved through hits, walks, or hits by pitch.

Sabermetrics had not been widely used ____ the Athletics adopted it in 2002.

With sabermetrics, the Athletics were able to identify underrated players with significant contributions to winning and built a ____ team on a small budget.

The success of the Athletics prompted other teams ____ quickly adopt sabermetrics, sparking a movement to utilize data not only in baseball but also in other sports.

Thus, sports data ____ emerged as a systematic approach to predict game outcomes.

In soccer, the Liverpool Football Club of the English Premier League is similar to the Oakland Athletics ____ baseball.

In 2010, Liverpool ____ a change in ownership.

While data analysis was already prevailing in the Premier League at that time, the new owner of the ____ who also owned a baseball team in the United States, aimed to push data analysis in soccer to new heights.

____ he demanded that the club form a new data team.

The new team included a physicist from Cambridge, a nuclear scientist from Harvard, and a former chess ____

What they had in common ____ significant expertise in data.

The team started to ____ data analysis in key areas of club management, including player recruitment, injury prevention, and strategy during the game.

The team analyzed large amounts of data to recruit players ____ fit the team’s style of play.

In training, they collected data on players’ movement patterns, heart rates, and vital ____ by using GPS trackers and sensors to prevent injuries.

During games, they used live data to ____ tactical decisions such as substitutions and formations.

The data team was ____ for its efforts when the Liverpool Football Club won the league championship for the first time in 30 years in 2020.

The emergence of ____ analytics did not go unnoticed by the Korean sports industry.

One notable example of success is ____ Korean women’s curling team.

Following its debut at the Winter ____ Games of 2014, the team recognized the need to enhance its sweeping technique.

____ melts the ice and makes the stone move faster.

____ too much ice melts, however, the stone moves too fast and misses the target.

On the other hand, if not enough ice melts, the stone stops before reaching the ____

The ____ of ice melting depends on the sweeping speed and pressure.

Applying too much pressure slows down the ____ speed, and focusing only on speed makes it hard to transfer enough force to the broom.

Finding the right balance between speed and pressure is important to melt the right ____ of ice.

To identify the most effective technique, the ____ team designed a sweeping measurement device.

The device consisted of infrared cameras and sensors attached ____ the players’ bodies, shoes, and brooms.

Through ____ device and motion-capture screens, foot pressure values and other data were obtained for analysis.

After monitoring changes in the temperature of the ice surface, the team concluded ____ speed was more economical than strength for sweeping.

The use of data analysis was critical in the team’s winning of a silver ____ at the 2018 Pyeongchang Winter Olympics.

The successful use of sports analytics ____ not limited to the curling team.

The ____ women’s archery team had dominated the sport for decades.

However, the team turned to sports analytics to maintain ____ edge over its rivals in preparation for the 2020 Tokyo Olympics.

The ____ developed a system to monitor players’ heart rates by using advanced visual computing technology to convert facial color variations into heart rates.

This data was used ____ psychological training to help players maintain stable heart rates during crucial moments.

The team also created an AI coach that helped adjust ____ form.

The team managers requested that the AI coach edit training videos of the players to ____ with practical analysis.

Players and ____ used the edited videos to analyze the players’ usual habits or weaknesses.

The active ____ of data analysis by the Korean women’s archery team handed them their ninth Olympic gold medal in a row in Tokyo.

There are still limitations to what data analysis ____ capture in games.

Factors such as team chemistry, which is related to how well ____ get along, will likely remain difficult to measure.

Similarly, predicting player performance in a match cannot be entirely accurate as players are humans and not ____

Still, sports analytics is undeniably elevating the level of play across various sports, and fans are enjoying this ____