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

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

How did they do ____

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

Sabermetrics ____ created in the late 1970s.

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

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

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

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

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

With sabermetrics, the Athletics were able to identify underrated players with ____ contributions to winning and built a strong 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 ____ analytics emerged as a systematic approach to predict game outcomes.

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

In 2010, Liverpool underwent a ____ 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 United States, aimed to push data analysis ____ soccer to new heights.

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

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

What they had in common was significant expertise ____ data.

The team started to apply data ____ in key areas of club management, including 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 vital ____ by using GPS trackers and sensors to prevent injuries.

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

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

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

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

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

____ melts the ice and makes the stone move faster.

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

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

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

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

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

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

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

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

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

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

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

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

However, the team turned to sports analytics to maintain its edge over its rivals ____ 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 color variations into ____ rates.

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

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

The team managers requested that the AI ____ 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 ____ Korean women’s archery team handed them their ninth Olympic gold medal in a row in Tokyo.

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

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

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

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