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

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

____ did they do it?

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

____ was created in the late 1970s.

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

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

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

It does ____ matter whether this is achieved through hits, walks, or hits by pitch.

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

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

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

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

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

In ____, Liverpool underwent a change in ownership.

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

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

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

What they had in common was ____ expertise in data.

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

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

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

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

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

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

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

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

Sweeping melts the ____ and makes the stone move faster.

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

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

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

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 ____ is important to melt the right amount of ice.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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