New York Liberty's Transition Defense Under Data Pressure: A Tactical Look at the Losing Streak

When the clock showed 4 minutes 32 seconds in the game on February 12, Han Xu was pulled out of the paint. The opponent ran a pick-and-roll, their center rolled to the basket, Han Xu chased. The three-point shot was taken, the ball went in, and the lead grew to 9. I replayed the tape four times to count every step Han Xu made in this sequence. The result showed she was out of position in three consecutive defensive possessions. This was not an isolated individual mistake; this was a system being exploited deliberately. The context of this losing streak began with the New York Liberty women's basketball team suffering 9 consecutive losses during the 2026 season. During that time, I reviewed all the footage from Second Spectrum and noticed a recurring pattern. Rookie center Han Xu was exploited an average of 14 times per game in pick-and-roll situations, and opponents scored an average of 1.17 points per possession against her position. This number was 0.23 points higher than the league average in the same situation. I calculated this based on raw data from the motion tracking system, not from the team's official statistics. The New York Liberty had lost 9 straight games, and I began investigating by cross-referencing data with game footage. I noticed that the problem was not Han Xu's ability but the way the defensive system was designed. When Han Xu was pulled out of the paint, the space in the restricted area became vast. Opponents only needed one cross-court pass to get the ball into this area, and their center had an easy scoring opportunity. Data showed that 78% of the points opponents scored in these situations came from near the basket, not from three-pointers. I counted the tape four times, and the error was the source's, not mine. The team's official stats credited Han Xu with 7 successful blocks in that game, but when I replayed the tape, I only counted 5. The discrepancy came from the automated data system counting invalid blocks. This made me question the entire way the team evaluated the defensive effectiveness of this young player. Tactically, head coach Sandy Brondello used Han Xu as a drop-back center in a switching defense system. This meant Han Xu often had to face pick-and-roll situations in the middle of the court. She did not have the necessary speed to chase opposing guards over a wide area. Data from Second Spectrum showed Han Xu's average reaction time in these situations was 1.8 seconds, while the league average was 1.2 seconds. This gap created space for opponents to attack. People see mistakes and laugh; I see mistakes and find the source. I reviewed all 9 losses and found that coach Sandy Brondello rarely adjusted defensive tactics during games. She only changed her approach after the game ended. In those 9 losses, only 2 games saw the team switch from switching defense to zone defense. Both games showed significantly better defensive ratings, allowing opponents an average of 98 points per 100 possessions compared to 112 points in the other games. Rebounds that the organization records incorrectly still count — if you bother to replay. I also discovered that the team's data system had misrecorded 15 rebounds across the entire losing streak. These discrepancies were not simply technical errors; they concealed the real issue of rebounding control. The paint was where opponents scored 61% of all points during this losing streak, and the actual number was even higher if corrected for the rebounding errors. After three weeks of investigation and podcast episodes, coach Sandy Brondello refused to grant an interview. However, the team began to change its tactics. Han Xu was kept closer to the basket to protect the paint. In the next 3 games, the team's defensive rating improved markedly, allowing an average of 101 points per 100 possessions. This showed that the problem was not the young player's ability but rather a tactical system that did not fit her physical attributes and speed. 31% of kilometers run toward the opponent's goal is the number I want to talk about. In this case, I want to talk about the 12.3 kilometers each player runs in a game, but only 31% of that was spent improving defensive position. This shows the team was running a lot but not running in the right direction. The effectiveness of a team is not measured by distance covered, but by the intelligence of each step. When the crowd disappears, young players' free throws disappear too — unless you're in the EuroLeague. From this perspective, I saw that the New York Liberty's problem was not just tactical but also psychological for the young players. Han Xu was only 23, and the pressure of the losing streak seemed to affect her decisions on the court. In the first 5 games of the streak, Han Xu attempted 4.5 free throws per game at an 82% rate; in the last 4 games, this dropped to 1.2 per game at a 71% rate. This decline showed she was avoiding contact instead of imposing her will. The thesis was rejected; data does not argue. When I published my findings, some said I was blaming a young player. I only replied that data does not look at age or reputation. Han Xu was exploited 14 times per game in pick-and-rolls, and opponents scored an average of 1.17 points per possession. These numbers are independent of my emotions or the team's. I wrote 19 pages and distilled them into one sentence worth saying. This podcast series reached 80,000 listens, 5 times the average episode. I was not surprised that audience interest surged after I pointed out the discrepancies in official data. Fans always crave the truth, and they are willing to listen to anyone patient enough to find it. Interestingly, the concept of 'discrepancy' in data can apply to politics as well. When I read analyses of political strategy, I notice that deliberate ambiguity is often confused with indecisiveness. But in sports, ambiguity in tactics is punished immediately on the court. The New York Liberty's opponents did not need to decipher the coach's intentions; they just exploited the space the defensive system created. I don't watch the 90th minute, I watch minute 1 to 90. My motto applies perfectly to analyzing the New York Liberty's losing streak. Instead of focusing only on decisive moments at the end of games, I watched the entire flow to find the root of the problem. Each opponent possession against Han Xu was a piece of a larger puzzle. When assembled, the picture revealed a lack of flexibility in the team's defensive system. When the crowd disappears, young players' free throws disappear too — unless you're in the EuroLeague. I mention this again because it relates directly to my finding. But I want to emphasize that Han Xu's problem was not just psychological. She needed to be supported by a defensive system that suited her abilities. When the team changed tactics and kept her closer to the basket, the team's defensive effectiveness improved markedly. This shows that the problem was not the player but the system design. Croatia is not the team that runs the most — they are the team that runs the most correctly. In basketball, the same principle applies. The New York Liberty ran a lot during the losing streak, but they ran in the wrong direction. They moved too much toward the ball handler instead of maintaining proper defensive position. This created space for opponents to exploit. When they changed their approach and prioritized position over speed, the results improved. I can say that the New York Liberty's losing streak is a lesson in the conflict between tactics and personnel. Coach Sandy Brondello had a tactical system she believed was correct, but that system did not fit Han Xu's physical profile. Instead of adjusting the system to fit the player, the team tried to force the young player to adapt to the system. This led to a regrettable losing streak. People see mistakes and laugh; I see mistakes and find the source. I found the root of the problem: a lack of flexibility in tactical thinking and an over-reliance on data without verifying reality. The team's data system recorded errors, but more importantly, they failed to recognize the real problem. They focused on improving incorrect metrics instead of solving the root issue. When the New York Liberty ended their losing streak with a new approach, I realized that the change was not just tactical. It was a change in how they viewed the problem. Instead of blaming a young player, they accepted that the system needed to adapt. When they did, things began to change for the better. My podcast about this losing streak did not only attract women's basketball fans, but also those interested in how data can be used to understand problems more deeply. I received an email from a data analyst for a major team, saying they faced similar issues with automated tracking systems. This shows that the New York Liberty's problem was not unique; it was an industry-wide lesson. I ended the podcast by saying: "I don't watch the 90th minute, I watch minute 1 to 90. And when I watch, I see that the truth always lies in the small details that few people notice." This holds true for basketball and for any other field where data and reality often have a gap that needs bridging. My analysis shows that the New York Liberty's problem lay in their transition defense system, specifically in how Han Xu was exploited in pick-and-roll situations. Data from Second Spectrum indicated she faced 14 of these situations per game and opponents scored an average of 1.17 points per possession. These numbers convinced me that this was a systemic issue, not an individual error. When the team changed tactics and kept Han Xu closer to the basket, the team's defensive efficiency improved significantly. This shows that adapting to the player was the key to solving the problem. The thesis was rejected; data does not argue. When I defended my thesis on the impact of empty arenas on free throw efficiency, I faced pushback from the committee. But I used data from 612 games to prove my point. In the case of the New York Liberty, I also used data from multiple sources to show that the problem was not the young player. The data spoke for itself. What pleased me most about this podcast series was that it created a meaningful dialogue about data and tactics. Fans began questioning the numbers they saw on screen, instead of accepting them blindly. This is a step forward in sports literacy, and I hope it continues to spread. I will continue to monitor the New York Liberty's situation for the rest of the season. Will the tactical changes be maintained when the team faces stronger opponents? Can Han Xu continue to develop in this new system? These questions can only be answered through continuous verification. And in my role, I will continue to replay the tape and cross-reference data to find the truth. In a broader context, this series of analysis also raises an important question about how we use data in sports. Are we over-reliant on numbers provided by automated systems without checking their accuracy? I believe the answer is yes, and this is negatively affecting how teams make decisions. When I look at coach Sandy Brondello's reaction to my findings, I see that change is never easy. She refused to grant an interview, but her actions spoke louder than words. Changing tactics after three weeks was a sign that she had accepted the problem, even if she did not publicly acknowledge it. This shows that data, when presented persuasively, can create change even in the face of initial resistance. I hope that the New York Liberty's story will serve as a reminder of the importance of data verification and tactical flexibility. In a world increasingly dependent on technology, we should not forget that behind every number is a human being with individual abilities and limitations. Only when we understand this can we create a healthy sports environment where data and people can thrive together. People see mistakes and laugh; I see mistakes and find the source. I will continue to do this, relentlessly, because it is the only way to find the truth in a world full of incorrect numbers.