Spring 2027

Advanced Sports Analytics

Advanced Sports Analytics uses statistical modeling and data science to derive insights from sports data, covering rankings, player contributions, and rule change simulations.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 7

Past student reviews · Ismor Fischer

The workload included lengthy assignments. Exams were open-book tests, a format that allowed students to reference materials during assessment.

What you’ll be able to do

  • Implement appropriate statistical methods to assess player and team performance.

  • Derive actionable insights about player and team skill from play-by-play and high-resolution tracking data.

  • Communicate analytical processes, results, and conclusions to technical and non-technical audiences through written reports and oral presentation.

  • Provide constructive feedback on your peer's written reports and oral presentations.

  • Build a personal portfolio of sports data analyses and research projects.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

STAT 333, 340, 601, 611,ACT SCI 640, or GEN BUS 656

STAT 441

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

ISMOR FISCHER is recorded teaching in Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
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Student experience

the class

Past student reviews · Ismor Fischer

The course involved open-book tests and lengthy assignments. One student found the provided packet helpful and noted that exams were similar to homework questions.

Recent recorded grades — Spring 2009: 3.30 GPA, 50.7% A/AB (n=134 letter grades); Spring 2010: 3.52 GPA, 71.6% A/AB (n=134 letter grades); Spring 2011: 3.49 GPA, 62.1% A/AB (n=124 letter grades).

difficulty & workload

Past student reviews · Ismor Fischer

The workload included lengthy assignments. Exams were open-book tests, a format that allowed students to reference materials during assessment.

Past student reviews · Ismor Fischer

One student described the teaching as terrible, citing incomprehensible lecture notes and confusion over textbook selection. They called it the worst taught class they had taken.

Topics

  • Team rankings from paired competitions and measuring individual player contributions to team success
  • Assessing player performance and team strategy via expected outcomes, and forecasting rule change impacts using simulation
  • Creating new metrics using high-resolution player tracking data

Skills

  • Implement statistical methods to assess player and team performance
  • Derive actionable insights from play-by-play and high-resolution tracking data
  • Communicate analytical results through written reports and oral presentations
  • Build a personal portfolio of sports data analyses and research projects

Grades

Latest available · Spring 2011— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2011

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2011 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

799 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records

No offering records for the selected term.

Raw records 
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "c623c9d457bc707e6eeb563646753acece9bcc44eea66d0154533355218f14ab",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}