<scp>B</scp>ayesian Methods in Sport Statistics

Fernandez, Edgar Santos; Mengersen, Kerrie; Wu, Paul

doi:10.1002/9781118445112.stat08179

Cited by 3 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Effect size (Cohen’s d factor) and confidence intervals were calculated with OriginPro 2016 Sr2 software (Northampton, MA, USA) and the post-hoc classification for effect size (based on modular Cohen’s d values) was: |d| ≥ 0.10, very small; |d| ≥ 0.20, small; |d| ≥ 0.50, medium; |d| ≥ 0.80, large; |d| ≥ 1.20, very large; and |d| ≥ 2.00, huge [ 36 ]. Bayesian inference analysis was performed based on prior information collected from published works, considering: BF10 < 3 (anecdotal); 10 > BF10 > 3 (moderate); 30 > BF10 > 10 (strong); 100 > BF10 > 30 (very strong); 300 > BF10 > 100 (extremely strong); BF10 > 300 (extreme) [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Uric Acid and Cortisol Levels in Plasma Correlate with Pre-Competition Anxiety in Novice Athletes of Combat Sports

et al. 2022

View full text Add to dashboard Cite

Pre-competition anxiety is very prevalent in novice athletes, causing stress and drastic decreases in their performances. Cortisol plays a central role in the psychosomatic responses to stress and also in the physiology of strenuous exercise. Growing evidence links uric acid, an endogenous antioxidant, with oxidative stress and anxiety, as observed in many depressive-related disorders. We here compared anxiety inventory scores (BAI and CSAI-2), cortisol and biomarkers of oxidative stress in the plasma of novice combat athletes (white and blue belts) before and after their first official national competition, when levels of stress are presumably high. Although the novice fighters did not reveal high indexes of anxiety on questionnaires, significant correlations were confirmed between cortisol and cognitive anxiety (Pearson’s r = 0.766, p-value = 0.002, and a ‘strong’ Bayesian inference; BF10 = 22.17) and between pre-post changes of plasmatic uric acid and somatic anxiety (r = 0.804, p < 0.001, and ‘very strong’ inference; BF10 = 46.52). To our knowledge, this is the first study to report such strong correlations between uric acid and pre-competition anxiety in novice combat athletes. The cause-consequence association between these indexes cannot be directly inferred here, although the interplay between uric acid and anxiety deserves further investigation.

show abstract

Section: Methodsmentioning

confidence: 99%

Uric Acid and Cortisol Levels in Plasma Correlate with Pre-Competition Anxiety in Novice Athletes of Combat Sports

et al. 2022

View full text Add to dashboard Cite

show abstract

“…A su vez, el coeficiente de determinación (R 2 ) fue determinado para expresar el porcentaje de cambio entre las variables analizadas (Borda Pérez et al, 2013). La magnitud de las correlaciones fue interpretada utilizando la d de Cohen utilizando los criterios propuestos por Hopkins et al, (2009) (Marsman & Wagenmakers, 2017;Quintana & Williams, 2018;Santos-Fernandez, Wu, & Mengersen, 2019). La interpretación de los resultados fue realizada de acuerdo según la clasificación de los valores de Jeffreys donde para H 1 : 1 a 3 se consideró [débil], 3 a 10 [moderado] y > 10 [fuerte] (van Doorn et al, 2020).…”

Section: Análisis Estadísticounclassified

Relación de la Asimetría Bilateral y el Déficit Bilateral con la Velocidad del Cambio de Dirección en Atletas Cadetes de Karate: Un estudio Piloto (Relationship of Bilateral Asymmetry and Bilateral Deficit with the Change of Direction Speed in Cadet Kara

Ojeda-Aravena

Azócar-Gallardo

Herrera-Valenzuela

et al. 2021

Retos

View full text Add to dashboard Cite

Antecedentes: La Asimetría Bilateral (AB) y el Déficit Bilateral (DBL) y su potencial relación negativa con el rendimiento físico es actualmente estudiada, aunque la relación con la velocidad del cambio de dirección (CODS) y el sprint lineal (5-M) en atletas de karate se desconoce. Objetivo: Examinar las correlaciones entre la AB y el DBL con el CODS y 5-M, así como entre el salto horizontal con el CODS y 5-M. Métodos: Diez atletas hombres de categoría cadetes participaron voluntariamente de este estudio. Entre las habilidades físicas evaluadas se incluyeron: salto horizontal bilateral (SBJ), salto horizontal unilateral derecho (RSBJ), salto horizontal unilateral izquierdo (LSBJ), CODS y 5-M. Posteriormente, la AB y el DBL fueron calculados. Resultados: Se reportó una baja AB (5.17 ± 3.61%) y presencia de DBL (-36.9 ± 6.2 %). Correlaciones moderadas entre la AB con CODS (r = -.38; R2 = .15%; p = .30) y 5-M (r = -.47; R2 = .22%; p = .19) y entre DBL con 5-M (r = .51; R2 = .26%; p = .16) y bajas con CODS (r = .20; R2 = .04%; p = .60). Correlaciones significativas (p < .05) entre CODS con RSBJ (r = -.92; R2 = .85%; p = .00), SBJ (r = -.87; R2 = .75%; p = .00) y LSBJ (r = -.82; R2 = .67%; p = .00). Conclusiones: La AB y el DBL se relacionarían negativamente con el CODS y 5-M. Aunque, el CODS se relaciona con la fuerza explosiva horizontal bilateral y unilateral. Abstract. Background: Bilateral asymmetry (AB) and Bilateral Deficit (DBL) and their potential negative relationship with physical performance is currently studied, although the relationship with change of direction speed (CODS) and linear sprint (5-M) in karate athletes is unknown. Aim: To examine correlations between AB and DBL with CODS and 5-M, as well as between horizontal jump with CODS and 5-M. Methods: Ten male cadet athletes voluntarily participated in this study. The physical abilities assessed included: bilateral horizontal jump (SBJ), right unilateral horizontal jump (RSBJ), left unilateral horizontal jump (LSBJ), CODS and 5-M. Subsequently, AB and DBL were calculated. Results: Low AB (5.17 ± 3.61%) and presence of DBL (-36.9 ± 6.2 %) were reported. Moderate correlations between AB with CODS (r = -.38; R2 = .15%; p = .30) and 5-M (r = -.47; R2 = .22%; p = .19) and between DBL with 5-M (r = .51; R2 = .26%; p = .16) and low with CODS (r = .20; R2 = .04%; p = .60). Significant correlations (p < .05) between CODS with RSBJ (r = -.92; R2 = .85%; p = .00), SBJ (r = -.87; R2 = .75%; p = .00) and LSBJ (r = -.82; R2 = .67%; p = .00). Conclusions: AB and DBL would be negatively related to CODS and 5-M. However, CODS is related to bilateral and unilateral horizontal explosive strength.

show abstract

“…In this direction, Bayesian methods make it possible to combine the information contained in the observed data and the subjectivity of different experts in sports [6]. The knowledge acquired from the experts will be added as a prior probabilistic distribution as, for example, these elements may consider aspects such as the expected recruitment of a player (to a certain team) or his/her physical performance.…”

Section: Introductionmentioning

confidence: 99%

Is Football/Soccer Purely Stochastic, Made Out of Luck, or Maybe Predictable? How Does Bayesian Reasoning Assess Sports?

Blanco

Ferreira

Louzada

et al. 2021

Axioms

View full text Add to dashboard Cite

Predicting the game score is a well-explored duty, using mathematical/statistical models. Nonetheless, by adopting a Bayesian methodology, this study aimed to estimate probabilistically the Chilean Premier League teams’ position, considering them a hierarchical structure. This approach enabled the evaluation of the main Chilean championship that provides the major soccer players for the national team. Thus, a countable (Poisson) regression structure was considered to explain each match as a combination of home advantage, added to the power of attack and defense of each team and considering their performance in the championship as an independent game. We were able to quantify the relationship across the defense and attack of each team and, in addition, were able to group/verify the performance of the entirety of the 2020 Chilean Premier League. For the model validation, we saved the last five games for the model prediction and we found that, in this league, the teams presented a statistical significance in the attack factors, which influences the scores (goals); however, all the teams showed low defense power and we have also found that playing at home or away did not present a game advantage. Our model was able to predict the Chilean league position table, with precision on the top five positions, and from the 6–11 positions there was a small shift (close performance in the championship) caused by the similarity of the expected number of goals, which implied the same position on the rank. This type of model has been shown to be very competitive for the soccer championship prediction.

show abstract

Bayesian Methods in Sport Statistics

Cited by 3 publications

References 36 publications

Uric Acid and Cortisol Levels in Plasma Correlate with Pre-Competition Anxiety in Novice Athletes of Combat Sports

Uric Acid and Cortisol Levels in Plasma Correlate with Pre-Competition Anxiety in Novice Athletes of Combat Sports

Relación de la Asimetría Bilateral y el Déficit Bilateral con la Velocidad del Cambio de Dirección en Atletas Cadetes de Karate: Un estudio Piloto (Relationship of Bilateral Asymmetry and Bilateral Deficit with the Change of Direction Speed in Cadet Kara

Is Football/Soccer Purely Stochastic, Made Out of Luck, or Maybe Predictable? How Does Bayesian Reasoning Assess Sports?

Contact Info

Product

Resources

About