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

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

____ did they do it?

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

____ was created in the late 1970s.

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

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

Sabermetrics suggests that players’ offensive skills can be better measured by the frequency with ____ 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 the Athletics ____ it in 2002.

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

The success of the Athletics prompted other teams to quickly ____ 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 in ____.

In 2010, ____ underwent a change in ownership.

While data analysis was ____ 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 in soccer to new heights.

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

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

____ they had in common was 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 who fit the team’s style of ____.

In training, they collected data on players’ movement patterns, ____ rates, and vital 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 Football ____ won the league championship for the first time in 30 years in 2020.

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

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

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

Sweeping melts the ice and makes the ____ move faster.

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

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

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

Applying too much pressure slows down the sweeping speed, and ____ 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.

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

The device consisted of ____ cameras and sensors attached 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, the ____ concluded that speed was more economical than strength for sweeping.

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

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

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

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

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

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

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

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

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

The active use of data ____ 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 can ____ in games.

____ such as team chemistry, which is related to how well 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 undeniably elevating the level of ____ across various sports, and fans are enjoying this development.