terça-feira, 16 de janeiro de 2024
Bia tem duelo dificil contra campeã dom Australin Open juvenil em 3023
Beatriz Haddad Maia enfrenta mais tarde Alina Korneeva em jogo válido pela segunda rodada do Australian Open. Bia nunca enfrentou a Korneeva. A russa venceu em 2023 o Australian Open e o Roland Garros Junior. Em março tornou-se a quinta mais jovem a vencer um ITF. Ela chegou a ser número #1 do 88 dela.
segunda-feira, 31 de março de 2014
The Best European Football League: What the CTQ’s and Minitab Can Tell Us
by Laerte de Araujo Lima, guest blogger
In 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 league—they were convinced by the results I shared.
But now they’ve 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 CTQ—Voice of the Customer
This 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 Customer
|
UEFA CL database variable associated with CTQ
|
More goals per game to make game more fun!
|
↑ Average goals scored per game
|
Offensive strategy, with more attempts to score goals.
|
↑ Average attempts on target per game
|
↑ Average goals scored per game
| |
| ↓ Average fouls committed per game | |
More effective use of game time.
|
↓ Average fouls committed per game
|
More “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 Database
The 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.
Country
|
League
|
# of teams
|
# of match’s per team
|
Web site
|
Spain (ES)
|
LIGA BBVA
|
20
|
38
| |
Italy (IT)
|
Serie A
|
20
|
38
| |
England (UK)
|
Barclays Premier League
|
20
|
38
| |
Germany (GE)
|
Bundesliga
|
18
|
34
|
Ranking Criteria and Methodology
Based on the CTQ factors, I performed an analysis of each league, looking at each team’s individual average values for each CTQ. This lets me compare not only the overall league average, but also the league’s 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 Assistant to perform One-Way ANOVA analysis. Assistant > Hypothesis test > One-Way ANOVA.
Based on the One-Way ANOVA analysis, I’m able to identify and position the leagues based on last season’s average values of CTQ variables per match.
Then, after compiling all results, I deployed a Decision Matrix (another Six Sigma tool) to assess each league on the CTQ variables. The position of the league in the analysis and its associated weight (1 / 5 / 10) will give a final score for each league
Average Number of Yellow & Red Cards
As the Assistant's output makes clear, the One Way ANOVA hypothesis test p-value (0.001) is less than the threshold ( < 0.05), which indicates that there is a difference among the means. The table to the right of the p-value calculation show us which means differ from others, and the means comparison chart gives the graphical view of the statistical analysis.
Conclusion: The U.K. football league has the lowest average of cards (Yellow & Red) per game.
Average Fouls Committed per Game
The p-value (0.001) is less than the threshold (< 0.05), telling us that there is a difference in means. In this case, based on the comparison chart, it’s evident that the U.K. league has the lowest average number of fault per game so far. Among the remaining three leagues, there’s not a statistically significant difference between the Spanish and Italian leagues, nor between the Italian and German. In this situation, I’ve decided to give to the Spanish league a different score than German and Italian.
Average Goals Scored per Game
The p-value (0.729) is greater than the threshold (< 0.05), indicating there is no significant difference in the means.
Conclusion: No matter which league you watch, the number of goals per match will be, on average, the same.
Conclusion: No matter which league you watch, the number of goals per match will be, on average, the same.
Average Attempts on Target per Game
Again, the test p-value result (0,891) is greater than the threshold ( < 0.05), telling us that there is no statistically significant difference in the average attempts on target per game.
Conclusion: All four leagues will will receive the same score for this variable.
The final decision matrix helps us see the results of all of these analyses:
Conclusion: The Best European Football League
Based on the results shown above, we can conclude the following.
- Normality is not an issue. Except for the German league (18), all data have a sample size of 20 teams by league, and are normally distributed.
- The U.K. league is the best in terms of “Fair Play.” Both average faults per game and cards (yellow & red) are less those than others leagues. (Now I can understand the critics of English support to the referee when an English team plays against another European team.)
- There is no difference among the leagues in terms of average attempts on target per game, nor average goals per match.
- Under the premise that the best European football league should have the best performance regarding the variables related to the selected CTQ, England’s football league comes out on top.
If a good league is one that answers the “customer” expectations (CTQ), the exercise performed in this post shows that England’s supporters (from all teams in premier league) should be the most satisfied supporters in Europe.
Unfortunately, this analysis is only based on the last season’s data, and thus it may only represent one static season and not a trend. But at minimum this analysis indicates that there are significant difference among the leagues, especially in the “fair play” CTQ factor.
About the Guest Blogger:
Laerte de Araujo Lima is a Supplier Development Manager for Airbus (France). He has previously worked as product quality engineer for Ford (Brazil), a Project Manager in MGI Coutier (Spain), and Quality Manager in IKF-Imerys (Spain). He earned a bachelor's degree in mechanical engineering from the University of Campina Grande (Brazil) and a master's degree in energy and sustainability from the Vigo University (Spain). He has 10 years of experience in applying Lean Six Sigma to product and process development/improvement. To get in touch with Laerte, please follow him on Twitter @laertelima or on LinkedIn.
domingo, 30 de março de 2014
Qual o melhor campeonato europeu de futebol ?
The Best European Football League: What the CTQ’s and Minitab Can Tell Us
by Laerte de Araujo Lima, guest blogger
In 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 league—they were convinced by the results I shared.
But now they’ve 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 CTQ—Voice of the Customer
This 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 Customer
|
UEFA CL database variable associated with CTQ
|
More goals per game to make game more fun!
|
↑ Average goals scored per game
|
Offensive strategy, with more attempts to score goals.
|
↑ Average attempts on target per game
|
↑ Average goals scored per game
| |
| ↓ Average fouls committed per game | |
More effective use of game time.
|
↓ Average fouls committed per game
|
More “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 Database
The 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.
Country
|
League
|
# of teams
|
# of match’s per team
|
Web site
|
Spain (ES)
|
LIGA BBVA
|
20
|
38
| |
Italy (IT)
|
Serie A
|
20
|
38
| |
England (UK)
|
Barclays Premier League
|
20
|
38
| |
Germany (GE)
|
Bundesliga
|
18
|
34
|
Ranking Criteria and Methodology
Based on the CTQ factors, I performed an analysis of each league, looking at each team’s individual average values for each CTQ. This lets me compare not only the overall league average, but also the league’s 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 Assistant to perform One-Way ANOVA analysis. Assistant > Hypothesis test > One-Way ANOVA.
Based on the One-Way ANOVA analysis, I’m able to identify and position the leagues based on last season’s average values of CTQ variables per match.
Then, after compiling all results, I deployed a Decision Matrix (another Six Sigma tool) to assess each league on the CTQ variables. The position of the league in the analysis and its associated weight (1 / 5 / 10) will give a final score for each league
Average Number of Yellow & Red Cards
As the Assistant's output makes clear, the One Way ANOVA hypothesis test p-value (0.001) is less than the threshold ( < 0.05), which indicates that there is a difference among the means. The table to the right of the p-value calculation show us which means differ from others, and the means comparison chart gives the graphical view of the statistical analysis.
Conclusion: The U.K. football league has the lowest average of cards (Yellow & Red) per game.
Average Fouls Committed per Game
The p-value (0.001) is less than the threshold (< 0.05), telling us that there is a difference in means. In this case, based on the comparison chart, it’s evident that the U.K. league has the lowest average number of fault per game so far. Among the remaining three leagues, there’s not a statistically significant difference between the Spanish and Italian leagues, nor between the Italian and German. In this situation, I’ve decided to give to the Spanish league a different score than German and Italian.
Average Goals Scored per Game
The p-value (0.729) is greater than the threshold (< 0.05), indicating there is no significant difference in the means.
Conclusion: No matter which league you watch, the number of goals per match will be, on average, the same.
Conclusion: No matter which league you watch, the number of goals per match will be, on average, the same.
Average Attempts on Target per Game
Again, the test p-value result (0,891) is greater than the threshold ( < 0.05), telling us that there is no statistically significant difference in the average attempts on target per game.
Conclusion: All four leagues will will receive the same score for this variable.
The final decision matrix helps us see the results of all of these analyses:
Conclusion: The Best European Football League
Based on the results shown above, we can conclude the following.
- Normality is not an issue. Except for the German league (18), all data have a sample size of 20 teams by league, and are normally distributed.
- The U.K. league is the best in terms of “Fair Play.” Both average faults per game and cards (yellow & red) are less those than others leagues. (Now I can understand the critics of English support to the referee when an English team plays against another European team.)
- There is no difference among the leagues in terms of average attempts on target per game, nor average goals per match.
- Under the premise that the best European football league should have the best performance regarding the variables related to the selected CTQ, England’s football league comes out on top.
If a good league is one that answers the “customer” expectations (CTQ), the exercise performed in this post shows that England’s supporters (from all teams in premier league) should be the most satisfied supporters in Europe.
Unfortunately, this analysis is only based on the last season’s data, and thus it may only represent one static season and not a trend. But at minimum this analysis indicates that there are significant difference among the leagues, especially in the “fair play” CTQ factor.
About the Guest Blogger:
Laerte de Araujo Lima is a Supplier Development Manager for Airbus (France). He has previously worked as product quality engineer for Ford (Brazil), a Project Manager in MGI Coutier (Spain), and Quality Manager in IKF-Imerys (Spain). He earned a bachelor's degree in mechanical engineering from the University of Campina Grande (Brazil) and a master's degree in energy and sustainability from the Vigo University (Spain). He has 10 years of experience in applying Lean Six Sigma to product and process development/improvement. To get in touch with Laerte, please follow him on Twitter @laertelima or on LinkedIn.
sexta-feira, 18 de outubro de 2013
Valência tenta empréstimo de Ilori
Valência tenta empréstimo de Ilori
Tiago Ilori, defesa central que no último verão trocou o Sporting pelo Liverpool por 7,5 milhões de euros, poderá rumar ao Valência na reabertura do mercado de transferências, em janeiro.
De acordo com a Imprensa inglesa, o clube espanhol está interessado no empréstimo do jogador, de 20 anos, até final da temporada, procurando tirar partido das poucas oportunidades que, à priori, o antigo leão terá para se impor em Anfield nos próximos meses.
O Valência conta nas fileiras com três internacionais portugueses. São eles Ricardo Costa, João Pereira e Hélder Postiga.
coluna do Tostão - Atletas e máquinas
coluna do Tostão - Atletas e máquinas
http://www.otempo.com.br/opini%C3%A3o/tost%C3%A3o/atletas-e-m%C3%A1quinas-1.729951
6858km de futebol http://twitter.com/6858kmdefutebol http://6858kmdefutebol.blogspot.com/
http://www.otempo.com.br/opini%C3%A3o/tost%C3%A3o/atletas-e-m%C3%A1quinas-1.729951
O Cruzeiro jogou mal e perdeu as duas últimas partidas. O São Paulo, com boa marcação mais atrás, e o Atlético, com ótima marcação mais à frente, por pressão, anularam todas as jogadas do time celeste. Borges não pegou na bola, porque ela não chegava, nem ele se movimentava. Borges só joga se a bola chegar a ele, pronta, para finalizar. Assim, até eu, com quase 70 anos. O Cruzeiro começa a ter as dificuldades de quem é líder. Além de um possível relaxamento, os adversários se preparam melhor para vencê-lo, ainda mais o grande rival. Se Ronaldinho e Jô estivessem presentes, o Atlético teria chance de fazer mais gols.
Cuca, mais uma vez, pinçou o jogador certo, Fernandinho. Muitos treinadores conhecem futebol, têm informações. Cuca entende o futebol. Se o Atlético enfrentasse, em casa, o Bayern, hoje, o melhor time do mundo, teria boas possibilidades de vencer, pela pressão que faz no Independência. Seria possível o Atlético, em um jogo heroico, no Marrocos, repetir o que consegue fazer no Horto? É pouco provável, mas não é impossível.
Cada dia mais, os principais times, em todo o mundo, marcam por pressão. Isso não significa que seja a única maneira eficiente de marcar e vencer. Quando o time que pressiona não consegue desarmar, é um desastre, pois se abrem grandes espaços na defesa. Além disso, há um grande desgaste físico. As equipes jogam no limite, entre o esplendor físico e a exaustão. Por isso, e pelo grande número de jogos, ocorrem muitas lesões musculares, mesmo com todos os avanços da ciência esportiva, como o de dosar substâncias indicadoras desse cansaço. Para amenizar as lesões, os principais clubes possuem grandes elencos. Imagine, daqui algum tempo, a intensidade que terá um jogo de futebol. Vai se tornar outro esporte. Provavelmente, a razão das seguidas lesões musculares de Messi seja por ele ter jogado muito, durante anos, sem ser poupado. Parafraseando Charles Chaplin, não sois máquinas, atletas é que sois.
A vitória por 2 a 0 sobre Zâmbia ajudou Felipão na definição do elenco para a Copa. Pato e Lucas devem ficar de fora. Pato só tem pose e fama. Com isso, abre uma vaga do meio para frente. Se o Brasil ganhar a briga com a Espanha, Diego Costa pode ser uma opção. Mas ficam três centroavantes (Jô, Fred e Diego Costa). Prefiro Tardelli. Os reservas Lucas Leiva, Maxwell e Diego Cavalieri são quase certos. Lucas Leiva é melhor que Fernando. Falta mais um zagueiro. Dedé ganhou pontos. Há ainda os jogadores de sobreaviso, caso alguém se machuque.
Jogo decisivo
Gosto mais do estilo do Cruzeiro atual do que o time celeste de 2003, e do Atlético da Libertadores. Mas, o Cruzeiro de 2003, por ter tido um cracaço, Alex, dez anos mais novo, e o Atlético da Libertadores, por ter contado com quatro jogadores com nível de seleção (Ronaldinho, Jô, Tardelli e Bernard), são superiores ao Cruzeiro atual.
Hoje é uma partida decisiva. Se o time celeste ganhar, independentemente dos resultados do Botafogo e de Grêmio, haverá uma grande tranquilidade e quase certeza do título.
Se o Cruzeiro perder mais uma vez, e um ou os dois times concorrentes vencerem, poderá haver uma desconfiança, um medo, uma intranquilidade, em relação ao título desse ano.
6858km de futebol http://twitter.com/6858kmdefutebol http://6858kmdefutebol.blogspot.com/
quinta-feira, 10 de outubro de 2013
CONTOS DO VAMPETA - VINHO PROIBIDO
CONTOS DO VAMPETA - VINHO PROIBIDO
6858km de futebol
http://twitter.com/6858kmdefutebol
http://6858kmdefutebol.blogspot.com/
quarta-feira, 9 de outubro de 2013
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