השוואת שיטות

	מודל בראדלי-טרי	מערכת דירוג אֵלוֹ	רגרסיה לוגיסטית
תחום	קבלת החלטות	קבלת החלטות	סטטיסטיקה למחקר
משפחה	Regression model	Regression model	Process / pipeline
שנת המקור	1952	1978	1958
הוגה השיטה	Ralph Bradley & Milton Terry	Arpad Elo	David Roxbee Cox
סוג	Probabilistic paired comparison model	Pairwise comparison ranking model	Method
מקור מכונן	Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4), 324–345.	Elo, A. E. (1978). The Rating of Chessplayers, Past and Present. Arco Publishing. ISBN: 978-0-668-04721-0	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
כינויים	BT Model, Bradley-Terry-Luce Model, Paired Comparison Model, İkili Karşılaştırma Modeli	Elo Rating System, Elo Chess Rating, Elo Skill Rating, Elo Derecelendirme Sistemi	logit model, binomial logistic regression, LR
קשורות	3	2	3
תקציר	The Bradley-Terry model is a probabilistic model for paired comparisons that assigns a latent strength parameter to each item and predicts the probability that one item beats another in a head-to-head contest. Introduced by Ralph A. Bradley and Milton E. Terry in 1952, it provides a principled statistical framework for ranking items from pairwise preference data, including incomplete comparison designs where not every pair is directly observed.	The Elo Rating System is a pairwise comparison-based ranking method developed by Hungarian-American physicist and chess master Arpad Elo and formally published in 1978. Originally designed to assess the relative skill levels of chess players, it assigns each competitor a numerical rating that rises or falls after each encounter based on the expected versus actual outcome. The system assumes that player performance follows a logistic distribution, enabling probabilistic predictions of match results and continuous rating refinement over time.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.