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SUMMARY:Too Complicated to Model? Missing Female Athletes Data - Hilal Iş
 ık
DTSTART;TZID=Europe/Zurich:20261023T094500
DTEND;TZID=Europe/Zurich:20261023T101500
DTSTAMP:20261111T040114Z
UID:pretalx-swiss-python-summit-2026-D9BNSD@talks.python-summit.ch
DESCRIPTION:Sports science and performance analytics have historically bee
 n built on datasets that underrepresent female athletes. In many cases\, w
 hen data showed structured variability linked to biological cycles\, this 
 was treated as a complication rather than something to incorporate into mo
 delling. As a result\, female athletes were often excluded from analysis f
 rameworks that could not easily handle this type of variation.\n\nAs an am
 ateur athlete and data analyst myself\, I see this very clearly in practic
 e. Many training guidelines\, performance models\, and recovery assumption
 s that are widely used today are still largely based on male dominated dat
 asets. Female athletes are not only underrepresented in the data\, but the
  way their performance varies over time is often not properly captured in 
 the models themselves. This leads to a situation where the structure of fe
 male athletic performance is simplified\, averaged out\, or ignored\, rath
 er than being understood as part of the signal.\n\nIn this talk\, I explor
 e this problem through different modelling approaches in Python\, comparin
 g standard methods with approaches that explicitly account for structured 
 variability\, such as cyclical representations and Generalized Additive Mo
 dels (GAMs). The focus is on how modelling choices shape what is considere
 d signal versus noise in athlete performance data.\n\nRecent research dire
 ctions already point toward more inclusive modelling approaches in sports 
 science\, including cycle aware performance analysis\, individualized trai
 ning response models\, and frameworks that better account for physiologica
 l variation in female athletes. These approaches aim to improve training a
 nd nutrition recommendations and reduce blind spots that arise when female
  athletes are not properly represented in the data.\n\nThe key question is
  no longer whether this structure exists\, but how we choose to model it a
 nd what becomes possible when we do.
LOCATION:Aula 4.101
URL:https://talks.python-summit.ch/swiss-python-summit-2026/talk/D9BNSD/
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