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

The Oakland Athletics were a ____ 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 model used for making decisions in ____.

Sabermetrics was ____ in the late 1970s.

It ____ to use large amounts of information to discover patterns, trends, and insights that are 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 more home ____ and extra-base hits.

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

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

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

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 adopt sabermetrics, sparking a movement to utilize data not only in baseball but ____ in other sports.

Thus, sports data analytics emerged as a ____ 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, Liverpool underwent a change in ____.

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.

So he demanded that the club form a new ____ 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 apply data analysis in key ____ of club management, including player recruitment, injury prevention, and strategy during the game.

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

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

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

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

The emergence of sports analytics ____ 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 ____ Games of 2014, the team recognized the need to enhance its sweeping technique.

Sweeping ____ the ice and makes the stone move faster.

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

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

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

Applying ____ much pressure 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 speed and pressure is important to melt the right ____ of ice.

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

The device consisted of infrared cameras and sensors attached to the ____’ 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 team concluded that ____ 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 2018 ____ Winter Olympics.

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

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

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

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

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

The team ____ created an AI coach that helped adjust 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 edited videos to analyze the players’ usual ____ or weaknesses.

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

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

Factors such as team chemistry, which is related to how well people get along, will ____ 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 ____ various sports, and fans are enjoying this development.