I provided ChatGPT's most advanced model (4o) with Jessica Hull's 1500m progression data from worldathletics.org and asked it graph and analyze the data.
ChatGPT wrote:
To analyze the data for anomalies, I'll look at the yearly progression and identify any significant deviations from the overall trend.
Data Overview:
2012: 4:28.11
2013: 4:27.60
2014: 4:20.66
2015: 4:20.49
2017: 4:13.48
2018: 4:08.75
2019: 4:01.80
2020: 4:00.42
2021: 3:58.81
2022: 3:59.31
2023: 3:57.29
2024: 3:50.83
Overall Trend:
The times generally improve each year, reflecting a typical progression as an athlete's performance improves over time.
Year-to-Year Improvement:
2012 to 2013: Small improvement (0.51 seconds).
2013 to 2014: Significant improvement (6.94 seconds).
2014 to 2015: Very small improvement (0.17 seconds).
2015 to 2017: Notable improvement over two years (7.01 seconds).
2017 to 2018: Significant improvement (4.73 seconds).
2018 to 2019: Significant improvement (6.95 seconds).
2019 to 2020: Small improvement (1.38 seconds).
2020 to 2021: Moderate improvement (1.61 seconds).
2021 to 2022: Slight worsening (0.50 seconds).
2022 to 2023: Improvement (2.02 seconds).
2023 to 2024: Large improvement (6.46 seconds).
Anomalies:
2015 to 2017: There was no recorded improvement for 2016, and the improvement from 2015 to 2017 was significant. The lack of 2016 data may suggest an anomaly or missing data point.
2021 to 2022: There is a slight worsening in the time, which is unusual compared to the overall trend of improvement. This could be due to various factors such as injuries, changes in training, or external conditions.
2023 to 2024: The large improvement of 6.46 seconds is quite significant, suggesting a notable change in performance or training.
Conclusion:
The data shows a consistent trend of improvement, with a few anomalies such as the missing data point for 2016, a slight worsening in 2022, and a significant improvement in 2024.
Further investigation into these anomalies could provide more context, such as changes in training regimes, health, or external conditions affecting performance.
If you need more detailed statistical analysis or further insights, let me know!
Next, I added similar progression data for Laura Muir (LM) and Faith Kipyegon (FK) for comparison, and then specifically asked ChatGPT identify any suspicious trends.
Note I included a bit of contextual information such as Kipyegon's extended 18-month layoff due to pregnancy before her 2020 season, which it initially identified as highly suspicious.
ChatGPT wrote:
Data for three runners (LM, FK, JH) was provided, including their fastest 1500m race time for each year.
Runners' performance trends were visualized and analyzed for potential suspicious improvements.
Initial Observations:
Runner A (LM):
- Showed steady improvement from 2008 to 2013.
- Maintained consistent performance with minor fluctuations from 2014 to 2024.
- Best performance time: 3:53.79 in 2024.
Runner B (FK):
- Steady improvement from 2010 to 2013.
- Some fluctuations but significant improvement in 2020.
- Best performance time: 3:49.04 in 2024.
Runner C (JH):
- Consistent improvement from 2011.
- Significant improvement in recent years, notably 2022 to 2024.
- Best performance time: 3:50.83 in 2024.
Percentage Decrease Analysis:
From 2011 to 2024:
LM: 16.17%
FK: 8.19%
JH: 19.25%
From 2015 to 2024:
LM: 2.04%
FK: 4.30%
JH: 11.39%
From 2020 to 2024:
LM: 1.52%
FK: 4.19%
JH: 3.99%
From 2023 to 2024:
LM: 0.58%
FK: 0.03%
JH: 2.72%
Suspicious Trends:
FK showed a significant improvement in 2020.
JH showed significant recent improvements.
Contextual Adjustments:
FK (Runner B): Pregnancy and an 18-month training break in 2018. Significant 2020 improvement is explained as regaining fitness.
JH (Runner C):
Not an Olympic finalist in 2021 but now ranked 5th all-time. Significant improvement from 2022 to 2023.
Final Conclusion:
Runner C (JH): Shows the most unusual and rapid improvement, particularly from 2021 to 2024, warranting further scrutiny.
Runner B (FK): Improvements align with the natural progression after a break due to pregnancy, reducing initial suspicion.
This summary encapsulates the analysis, highlighting the key findings and contextual considerations for each runner's performance trends.
If further details or specific aspects need more focus, please let me know!
Discus.
