The Best European Football League: What the CTQs and Minitab Can Tell Usby Laerte de Araujo Lima, guest bloggerIn a previous post (How Data Analysis Can Help Us Predict This Year's Champions League), I shared how I used Minitab Statistical Software to predict the 2013-2014 season of the UEFA Champions league. This involved the regression analysis of main critical-to-quality (CTQ) factors, which I identified using the voice of the customer suggestions of some friends.Since that post was published, my friends have stopped discussing the UEFA Champions leaguethey were convinced by the results I shared.But now theyve challenged me to use Six Sigma tools to quantify which European football league is best. In other words, which league gives its fans the best value (average per game) in terms of the CTQ factors that make games fun to watch?Critical to the CTQVoice of the CustomerThis analysis will be based on the same CTQ factors used in my previous post. I debriefed my friends (in a bar, while watching a football match, of course) after publishing that post, and they agreed that these CTQ really match their expectations about what should and should not happen in a match.However, I did add one new CTQ factor in this study, Average Number of Yellow and Red Cards, since these data were available in a new database.CTQ Voice of CustomerUEFA CL database variable associated with CTQMore goals per game to make game more fun! Average goals scored per gameOffensive strategy, with more attempts to score goals. Average attempts on target per game Average goals scored per game Average fouls committed per gameMore effective use of game time. Average fouls committed per gameMore fair play and protection for players with high football skills. Average fouls committed per game Average number of Yellow and Red cards European Football League DatabaseThe hardest part of this study was finding a reliable and complete database. For this, my friends at www.whoscored.com proved best. In this database, I could find all variables associates with the previous defined CTQ. I apologize to my Portuguese and French friends, but as I noted in my previous post, only the most predominant countries and leagues (Italy, England, Germany, and Spain) in the last 12 UEFA Championship League seasons are considered in this scenario.CountryLeague# of teams# of matchs per teamWeb siteSpain (ES) LIGA BBVA2038http://www.ligabbva.comItaly (IT) Serie A2038http://www.legaseriea.it/England (UK) Barclays Premier League2038http://www.premierleague.com/en-gb.htmlGermany (GE) Bundesliga1834http://www.bundesliga.de/de/index.php Ranking Criteria and MethodologyBased on the CTQ factors, I performed an analysis of each league, looking at each teams individual average values for each CTQ. This lets me compare not only the overall league average, but also the leagues teams average.To perform this analysis, I used the statistical tool called Analysis of Variance (ANOVA). ANOVA tests the hypothesis that the means of two or more populations are equal.ANOVA evaluates the importance of one or more factors by comparing the response variable means at the different factor levels. The null hypothesis states that all population means (factor level means) are equal while the alternative hypothesis states that at least one is different.For this analysis I used the