Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans

Feely, Ciara; Caulfield, Brian; Lawlor, Aonghus; Smyth, Barry

doi:10.1007/978-3-030-58342-2_5

Cited by 23 publications

(8 citation statements)

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“…Although such data was not available in our dataset, the increasingly widespread adoption of mobile devices, smartwatches, and wearable sensors [55,56] has the capacity to generate large volumes of additional data (heart-rate, cadence, and power), which may be useful in this regard in the future [57,58]. Already, the availability of such diverse sources of data is enabling several new types of health and fitness applications [59][60][61][62][63] and the emergence of powerful new machine learning techniques has been used to support a variety of related prediction and planning tasks in several sporting domains [64][65][66][67][68][69][70][71][72][73] It is also worth noting that the model of the wall analysed here is defined by a pair of parameters-degree of slowdown and length of slowdown-with specific values-0.25 and 5km, respectively-and it is reasonable to question whether the results would be different if different values had been chosen. We have considered several alternative sets of values and, within reasonable levels of tolerance, there is no material change to the nature of the results as presented.…”

Section: Limitationsmentioning

confidence: 99%

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Smyth

2021

PLoS ONE

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Introduction In the marathon, how runners pace and fuel their race can have a major impact on race outcome. The phenomenon known as hitting the wall (HTW) refers to the iconic hazard of the marathon distance, in which runners experience a significant slowing of pace late in the race, typically after the 20-mile mark, and usually because of a depletion of the body’s energy stores. Aim This work investigates the occurrence of significant late-race slowing among recreational marathoners, as a proxy for runners hitting the wall, to better understand the likelihood and nature of such slowdowns, and their effect on race performance. Methods Using pacing data from more than 4 million race records, we develop a pacing-based definition of hitting the wall, by identifying runners who experience a sustained period of slowing during the latter stages of the marathon. We calculate the cost of these slowdowns relative to estimates of the recent personal-best times of runners and compare slowdowns according to runner sex, age, and ability. Results We find male runners more likely to slow significantly (hit the wall) than female runners; 28% of male runners hit the wall compared with 17% of female runners, χ2(1, N = 1, 928, 813) = 27, 693.35, p < 0.01, OR = 1.43. Such slowdowns are more frequent in the 3 years immediately before and after a recent personal-best (PB) time; for example, 36% of all runners hit the wall in the 3 years before a recent PB compared with just 23% in earlier years, χ2(1, N = 509, 444) = 8, 120.74, p < 0.01, OR = 1.31. When runners hit the wall, males slow more than females: a relative slowdown of 0.40 vs. 0.37 is noted, for male and female runners, when comparing their pace when they hit the wall to their earlier race (5km-20km) pace, with t(475, 199) = 60.19, p < 0.01, d = 0.15. And male runners slow over longer distances than female runners: 10.7km vs. 9.6km, respectively, t(475, 199) = 68.44, p < 0.01, d = 0.17. Although, notably the effect size of these differences is small. We also find the finish-time costs of hitting the wall (lost minutes) to increase with ability; r2(7) = 0.91, p < 0.01 r2(7) = 0.81, p < 0.01 for male and female runners, respectively. Conclusions While the findings from this study are consistent with qualitative results from earlier single-race or smaller-scale studies, the new insights into the risk and nature of slowdowns, based on the runner sex, age, and ability, have the potential to help runners and coaches to better understand and calibrate the risk/reward trade-offs that exist as they plan for future races.

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Section: Limitationsmentioning

confidence: 99%

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Smyth

2021

PLoS ONE

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“…In this context, an ontology helps to assure the correctness of recommendations, for example, in terms of the proposed medical diagnosis. Feely et al ( 2020 ) introduce a case-based recommendation approach for the recommendation of marathon training plans and race pacings based on case information about the workouts and race histories of similar runners. CEBRA (Hernandez-Nieves et al, 2021 ) supports the recommendation of banking products using a basic case-based reasoning recommender operating on a user's financial service profile and corresponding demographic data.…”

Section: Basic Approaches and Applicationsmentioning

confidence: 99%

“…Case-based (CBR) Jannach, 2006 Goeker andThompson, 2000;Bridge, 2002;McSherry, 2003;Mirzadeh et al, 2005;Christakopoulou et al, 2016 Khan andHoffmann, 2003;Zou et al, 2020Fesenmaier et al, 2003Lee and Kim, 2015;Musto et al, 2015;Feely et al, 2020;Bokolo, 2021;Hernandez-Nieves et al, 2021 Critiquing-based (CRIT) Reilly et al, 2004;Smyth et al, 2004;McCarthy et al, 2005;Mandl and Felfernig, 2012;Murti et al, 2016Zhang et al, 2008Chen et al, 2017;Xie et al, 2018;Wu et al, 2019;Güell et al, 2020McCarthy et al, 2010Burke et al, 1996Grasch et al, 2013 Constraint-based (CON) Cöster et al, 2002;Felfernig and Burke, 2008;Falkner et al, 2011;Fargier et al, 2016;Erdeniz et al, 2019;Teppan and Zanker, 2020;Felfernig et al, 2023a Towle andQuinn, 2000;Fano and Kurth, 2003;Junker, 2004;Felfernig et al, 2009bFelfernig et al, , 2012…”

Section: Approach Alg Pref Ka Appmentioning

confidence: 99%

“…Beyond case-based recommendation, such approaches to attribute value recommendation are also applied in constraintbased recommendation (and beyond) (Felfernig and Burke, 2008;Falkner et al, 2011;Fargier et al, 2016;Pereira et al, 2016;Temple et al, 2017). A major issue in such contexts is the fact that a recommendation of one or more attribute settings could be inconsistent with the underlying set of constraints (C).…”

Section: Recommending Preference Settingsmentioning

confidence: 99%

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Knowledge-based recommender systems: overview and research directions

Uta,

Felfernig,

et al. 2024

Front. Big Data

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Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

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“…These runners were all users of the popular running app, Strava, and this work is an example of the new type of data-driven research that has been enabled by the availability of wearable technology, body sensors, and smart watches/phones, which are now commonly used to record the exercise and activity habits of millions of people around the world. Indeed the availability of similar datasets has already facilitated a number of large-scale studies of marathon training and performance ( 14 – 16 ), and even offered the opportunity to develop personalised training recommendations for marathon runners ( 17 – 20 ).…”

Section: Introductionmentioning

confidence: 99%

Estimating the cost of training disruptions on marathon performance

Feely

Smyth

Caulfield

et al. 2023

Front. Sports Act. Living

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Completing a marathon usually requires at least 12–16 weeks of consistent training, but busy lifestyles, illness or injury, and motivational issues can all conspire to disrupt training. This study aims to investigate the frequency and performance cost of training disruptions, especially among recreational runners. Using more than 15 million activities, from 300,000 recreational runners who completed marathons during 2014–2017, we identified periods of varying durations up to 16 weeks before the marathon where runners experienced a complete cessation of training (so-called training disruptions). We identified runners who had completed multiple marathons including: (i) at least one disrupted marathon with a long training disruption of ≥7 days; and (ii) at least one undisrupted marathon with no training disruptions. Next, we calculated the performance cost of long training disruptions as the percentage difference between these disrupted and undisrupted marathon times, comparing the frequency and cost of training disruptions according to the sex, age, and ability of runner, and whether the disruptions occurred early or late in training. Over 50% of runners experienced short training disruptions up to and including 6 days, but longer disruptions were found to be increasingly less frequent among those who made it to race-day. Runners who experience longer training disruptions (≥7 days) suffer a finish-time cost of 5–8% compared to when the same runners experienced only short training disruptions (<7 days). While we found little difference (<5%) in the likelihood of disruptions—when comparing runners based on sex, age, ability, and the timing of a disruption—we did find significant differences in the the cost of disruptions (10–15%) among these groups. Two sample t-tests indicate that long training disruptions lead to a greater finish-time cost for males (5%) than females (3.5%). Faster runners also experience a greater finish-time cost (5.4%) than slower runners (2.6%). And, when disruptions occur late in training (close to race-day), they are associated with a greater finish-time cost (5.2%) than similar disruptions occurring earlier in training (4.4%). By parameterising and quantifying the cost of training disruptions, this work can help runners and coaches to better understand the relationship between training consistency and marathon performance. This has the potential to help them to better evaluate disruption risk during training and to plan for race-day more appropriately when disruptions do occur.

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Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans

Cited by 23 publications

References 23 publications

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Knowledge-based recommender systems: overview and research directions

Estimating the cost of training disruptions on marathon performance

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