The document discusses how data can help runners improve their performance by analyzing a large marathon dataset containing over 3 million race records. Some key insights from the data include that women and men benefit from different pacing strategies, many runners hit a wall around the 30km mark, and fast starts often lead to slow finishes. The data is also used to predict personal best times for individual runners based on their past race performance and recommend training plans to achieve those predicted times.

1. Running with Data
How data can help us to run faster
Barry Smyth
Insight Centre for Data Analytics
School of Computer Science
University College Dublin
@barrysmyth

2. Running a marathon is crazy,
fantastic, madness, hard!
But …
~ 2,000 marathons/yr globally
~ 2-3m participants/yr
26.2m / 42.2km on race-day
500 – 1,000km during 12/16
week pre-race training

3. Creating a Marathon Dataset
Discovering Data Insights

5. Summary Data Set
~ 3 million complete race records, all with 5km splits.
>100 individual races x 20+ cities x 3-12 years.
Age ranges ⇒ estimate YoB and ages.
Sex, country, club etc.
Repeat runner identification (name, sex, YoB).

6. Discovery & Insights

7. Pacing Men & Women

9. Run with a Pacing Group

10. Dublin (2013–2015)
With 10-min pacers
Dublin (2009–2011)
With 15-min pacers

11. Hitting the Wall

12. Boston 2017

13. Fast Starts & Slow Finishes
(J. Sports Analytics, to appear 2018)

16. Predicting Personal Bests
(ACM RecSys 2017, ICCBR 2017, IJCAI 2018)

17. Running a Personal-Best (PB)
Assume a single past marathon
race-record with 5km split-times.
Dual prediction/recommendation tasks:
1. Predicting challenging but achievable PB times.
2. Recommending a race-plan to achieve this PB.

18. Predicting PBs
Minimal Feature-Set (MR race only)
Maximal Feature-Set (MR_LR_PW_MV_LV_mnPB)

19. medium.com/running-with-data