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gender_notes.md

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  1. Introduction • Context: Briefly describe what ultramarathons are, how they have grown in popularity, and why examining gender trends is insightful. • Objectives: State the main goals of the analysis (e.g., trends in participation, finish times, performance levels, and any noteworthy shifts by decade).

  2. Data and Methodology • Data Source: Explain how you acquired the dataset (e.g., multiple race websites, historical archives). Mention it spans from the 1950s onward. • Data Fields: Provide a short list of relevant variables (e.g., year, runner ID, gender, age group, distance, finish time, average speed, performance ratio). • Data Cleaning & Preparation: Outline how you handled missing data or anomalies (e.g., different naming conventions, missing gender info, etc.). • Analytical Approach: Describe the methods or tools (e.g., Python, R, spreadsheets) used to parse trends over time.

  3. Historical Evolution of Participation

    1. Overall Growth • Chart the total number of participants per year (or decade) and split by gender. • Highlight key eras when participation spiked, and discuss possible reasons (e.g., introduction of more inclusive race policies, cultural shifts in athletics).
    2. Gender Ratio Over Time • Investigate the changing ratio of female to male participants, especially from the 1970s–1980s onward, when women’s distance running became more recognized. • Note any significant events or rule changes (e.g., official acceptance of women’s entries in certain ultramarathons).

  4. Performance Trends

    1. Finishing Times • Compare median or average finishing times for men vs. women over the years. • Identify whether the gap is shrinking or remaining consistent.
    2. Podium vs. General Field • Look at top finishers (e.g., top 5 or top 10) across the decades to see if women have been placing higher in the overall field. • Track if elite women’s performance has converged with men’s at the front of the pack.
    3. Performance Ratios • Utilize a performance ratio or finishing percentage (as shown in your dataset) to highlight relative performance across years. • This metric can help normalize across varying race lengths and conditions.

  5. Age Groups and Gender

    1. Demographics by Age Group • Break down participants by age group (e.g., 20–29, 30–39, 40–49, 50+). • Investigate how the age distribution differs between men and women and if one group tends to skew older or younger over time.
    2. Performance by Age • Examine whether older women vs. older men have different finishing-time patterns. • Highlight any particularly strong age brackets for either gender (e.g., “Women aged 40–49 have shown the most improvement over the last decade”).

  6. Distances and Terrains

    1. Gender Differences in Preferred Races • If your dataset includes multiple ultramarathon distances (e.g., 50km vs. 100km vs. 100mi), compare gender breakdowns by distance. • See if women are more or less likely to take on longer distances.
    2. Road vs. Trail • If you have terrain data (e.g., “road” vs. “trail”), analyze differences in men’s and women’s participation or performance for each. • Discuss whether certain terrains are more popular or produce tighter performance gaps for either gender.

  7. Interesting or Surprising Findings • Decade-Specific Milestones: Were there years when women’s finishing times made a notable jump? Did that coincide with new race records or high-profile female ultrarunners? • Exceptional Outliers: Identify any particularly remarkable performances (e.g., a woman finishing in the top 3 overall in a historically male-dominated event). • Participation Dips or Surges: Investigate any unexpected fluctuations (e.g., dips during certain economic or global events, or surges after significant female achievements in distance running).

  8. Potential External Factors • Cultural Shifts and Media Coverage: Touch on how social acceptance of women’s endurance sports has evolved, influencing the data. • Technological/Training Advances: Consider whether gear, nutrition, and training improvements have benefited one group more than the other. • Changes in Race Policies: Some events might have started as men-only or had unofficial female participation. Acknowledge how official acceptance impacted the data.

  9. Conclusion and Future Outlook • Summary of Key Observations: Recap the major trends: Has the gender gap narrowed in finishing times? Has women’s participation substantially grown in recent decades? • Implications: Reflect on what the data suggests about equality in ultrarunning and potential areas of continued growth. • Future Research: Mention possible deeper dives—e.g., effects of training regimes, sponsorship changes, or race climate conditions on performance.

Points Worth Investigating in Depth • Participation Rate by Decade: Identify clear patterns in the share of female runners, and hypothesize drivers (policy, culture, media). • Performance Gap Over Time: Analyze whether the difference in average finishing times or performance ratios is shrinking. • Top 1% vs. Median: Sometimes the front-runners show different trends than the overall field. • Age-Focused Insights: Women often peak in endurance events later than men; see if the data supports that, and whether that peak age has shifted over time. • Race-Type Preferences: Maybe more women choose extremely long trail events; or maybe the data show equivalently distributed preferences. • Number of 'new' runners per year based on runner_id