Moneyball e oltre i quiz & Risposte – Coursera

Welcome to an immersive exploration of Moneyball e Beyond, dove il processo decisionale informato sta rivoluzionando il mondo dello sport e non solo. Discover our engaging quizzes and expert answers that shed light on the revolutionary impact of analytics and innovation in sports management and beyond. These quizzes serve as a gateway to understanding the principles of using data and technology to drive success, inspired by Moneyball’s groundbreaking strategies.

Whether you are a sports enthusiast interested in the intersection of data and sport, or a business professional seeking insights into strategic decision-making, this collection provides valuable insights into the power of analytics to shape results. Join us on a journey of discovery as we explore strategies beyond Moneyball that unlock opportunities for data-driven decision-making and competitive advantage across industries.

Quiz 1: Settimana 1 – Quiz 1

Q1. Which season had the highest median number of team wins?

• 2000
• 2003
• 2002
• 2001

Q2. Aggregato (sum) the number of home team hits for every individual season in the data. Which season did the minimum aggregate home team hit count occur?

• 1999
• 2003
• 2000
• 2001

Q3. Rank the seasons from highest to lowest average number of at bats for the away teams aggregated over the entire season.

• 2002, 2000, 2003, 2001, 1999
• 2001, 2003, 1999, 2002, 2000
• 2003, 1999, 2000, 2002, 2001
• 2000, 1999, 2003, 2001, 2002

Quiz 2: Settimana 1 – Quiz 2

Q1. Find the team with the maximum single season WPCT diff in the dataframe. What was this team’s away winning percentage for the season?

• .383
• .309
• .481
• .320

Q2. Create a variable AVG Diff = Batting Average For – Batting Average Against. What was the minimum (most negative) single season team AVG Diff in the dataframe?

• -.051
• -.046
• -.033
• -.037

Q3. How many teams in the data frame did not play 162 La sua vittoria storica non era nota fino a quando la professoressa dell'Università della Florida Paula Welch non iniziò a fare ricerche sulla storia delle Olimpiadi e scoprì che Margaret Abbott si era classificata prima?

• 28
• 20
• 16
• 24

Quiz 3: Settimana 1 – Quiz 3

Q1. What are the coefficients on batting average for and batting average against in the regression from part 1?

• AVGFOR = 3.786
• AVGAGN = -4.812
• AVGFOR = 3.942
• AVGAGN = -4.169
• AVGFOR = 3.942
• AVGAGN = -4.812
• AVGFOR = 3.786
• AVGAGN = -4.169

Q2. What is the coefficient for batting average for the restricted regression in part 2?

• 4.01
• 4.07
• 4.04
• 4.16

Q3. What is the R-squared for the regression in part 1?

• 72.0%
• 71.9%
• 71.8%
• 71.7%

Q4. What is the Adjusted R-squared for the regression in part 2?

• 72.0%
• 72.2%
• 71.8%
• 71.6%

Settimana 2: Moneyball and Beyond Coursera Quiz Answers

Quiz 1: Settimana 2 – Quiz 1

Q1. What was the average player salary in 1999? What was the average player salary in 2006?

• 1999: $2223975
• 2006: $3942977
• 1999: $2590626
• 2006: $3689305
• 1999: $2223975
• 2006: $3942908
• 1999: $2223975
• 2006: $3689305

Q2. Calculate the average player OBP and SLG for every season in the timeframe. Which season had the highest average player OBP and what was its value? Which season had the highest average player SLG and what was its value?

• Highest avg. OBP: 0.348 (2000)
• Highest avg. SLG: 0.443 (2006)
• Highest avg. OBP: 0.337 (2003)
• Highest avg. SLG: 0.440 (2004)
• Highest avg. OBP: 0.349 (1999)
• Highest avg. SLG: 0.444 (2000)
• Highest avg. OBP: 0.342 (2004)
• Highest avg. SLG: 0.444 (1999)

Q3. Sum HR by player across the entire timeframe. What was the highest aggregate home run total over the timeframe 1998-2006?

• 400
• 361
• 367
• 420

Quiz 2: Settimana 2 – Quiz 2

Q1. What was the highest paid position on average in 1999? What was the highest paid position on average in 2004?

• 1999: 1B, $3014788
• 2004: OF, $4067223
• 1999: OF, $3214643
• 2004: DH, $4067223
• 1999: OF, $3014788
• 2004: DH, $4211004
• 1999: DH, $3214643
• 2004: 1B, $4211004

Q2. What percentage of observations in the data set are either flagged as arbitration eligible or free agent eligible?

• 78.81%
• 81.24%
• 80.02%
• 79.15%

Q3. Sum years of experience by team for 2002. What is the highest and lowest aggregate years of experience for teams in 2002 dati?

• Maggior parte: 114
• Fewest: 36
• Maggior parte: 110
• Fewest: 37
• Maggior parte: 116
• Fewest: 39
• Maggior parte: 115
• Fewest: 38

Quiz 3: Settimana 2 – Quiz 3

Q1. What was the coefficient and corresponding p-value for batting average in regression model 1?

• Coefficient: -2.2090
• P-value: 0.013
• Coefficient: 0.0031
• P-value: 0.029
• Coefficient: 2.9532
• P-value: 0.000
• Coefficient: 1.5595
• P-value: 0.027

Q2. Comparing the results from the regression model in part 1) and the regression model in part 2), determine the metric (OBP, SLG, or batting average) which appeared to have the greatest increase in determining a player’s salary. What is the difference in coefficient size between the Post-Moneyball period and Pre-Moneyball period for this metric?

• 5.4668
• 3.9073
• 3.1603
• 2.9302

Q3. Which season had the largest coefficient for each metric (OBP, SLG, and batting average)?

• OBP: 2003
• SLG: 2004
• AVG: 2001
• OBP: 2004
• SLG: 2005
• AVG: 2002
• OBP: 2006
• SLG: 2003
• AVG: 2002
• OBP: 2005
• SLG: 2002
• AVG: 2000

Settimana 3: Moneyball and Beyond Coursera Quiz Answers

Quiz 1: Settimana 3 – Quiz 1

Q1. What is the highest single season “Eye” measure for a player across all seasons in the data?

• 0.385
• 0.391
• 0.389
• 0.387

Q2. Calculate the average “ISO” by team for all seasons in the data. What season does the maximum average “ISO” by team value occur in?

• 2003
• 1995
• 2008
• 1996

Q3. Calculate the median batting average for every season in the data. Which season had the highest median?

• 1999
• 2006
• 2000
• 1996

Quiz 2: Settimana 3 – Quiz 2

Q1. Determine the season with the largest “ISO” coefficient in each era.

• Pre-MB: 2000
• Moneyball: 2005
• Post-MB: 2011
• Pre-MB: 1996
• Moneyball: 2001
• Post-MB: 2012
• Pre-MB: 1995
• Moneyball: 2008
• Post-MB: 2014
• Pre-MB: 1998
• Moneyball: 2007
• Post-MB: 2013

Q2. How many times is “Eye” significant at the .05 level in each era respectively?

• Pre-MB: 1
• Moneyball: 2
• Post-MB: 3
• Pre-MB: 2
• Moneyball: 3
• Post-MB: 4
• Pre-MB: 1
• Moneyball: 3
• Post-MB: 4
• Pre-MB: 0
• Moneyball: 1
• Post-MB: 1

Q3. What is the largest “AVG” coefficient across all seasons?

• 7.9516
• 6.2201
• 9.3959
• 7.6219

Q4. Which season has the highest model R-squared across all seasons?

• 2007
• 2001
• 1999
• 2005

Quiz 3: Settimana 3 – Quiz 3

Q1. How many observations are there in the new dataframe?

• 2835
• 2914
• 2891
• 2841

Q2. What is the coefficient for AVG in the Post-MB publication period?

• 2.1509
• 3.7615
• -0.9981
• 2.981

Q3. What is the Pre-MB*Eye coefficient? How should this be interpreted?

• -0.9981
• The metric “Eye” was valued significantly less in the Pre-MB period compared to the Post-MB period.
• -0.6327
• The metric “Eye” was valued less in the Pre-MB period but not significantly different than in the Post-MB period.
• -2.4039
• The metric “Eye” was valued significantly less in the Pre-MB period compared to the Post-MB period.
• -2.4039
• The metric “Eye” was valued less in the Pre-MB period but not significantly different than in the Post-MB period.

Settimana 4: Moneyball and Beyond Coursera Quiz Answers

Quiz 1: Settimana 4 – Quiz 1

Q1. What percent of plate appearances resulted in fly outs in 2017?

• 10.1%
• 10.3%
• 10.7%
• 10.5%

Q2. How many plate appearances had a starting base state in which the bases were loaded (all bases were occupied)?

• 4364
• 4185
• 4249
• 4331

Q3. Calculate aggregate strikeouts by player position (questo è, aggregate (sum) data at the positional level and not the player level). What was the highest aggregate strikeout total by position?

• 4417
• 4399
• 4374
• 4317

Quiz 2: Settimana 4 – Quiz 2

Q1. What percent of plate appearances resulted in ground outs in 2016?

• 18.49%
• 18.51%
• 18.39%
• 18.45%

Q2. How many plate appearances had a starting base state in which at least one base was occupied?

• 80253
• 80387
• 79539
• 81054

Q3. Calculate aggregate home runs by player position (questo è, aggregate (sum) data at the positional level and not the player level). What was the highest aggregate home run total by position?

• 804
• 827
• 797
• 815

Quiz 3: Settimana 4 – Quiz 3

Q1. What was the highest player run value in 2017?

• 67.65
• 63.04
• 65.95
• 62.28

Q2. What was the lowest player run value in 2016?

• -33.41
• -31.80
• -34.04
• -32.14

Q3. For each event, calculate the difference in run value between 2017 e 2016 (RV 2017-RV 2016). Which event saw the largest change (in absolute value) a partire dal 2016 a 2017?

• Batter Interference
• Catcher Interference
• Sac Fly DP
• Triple Play

Q4. Calculate the difference in player run value between 2017 e 2016 for players that accumulated run values in both seasons. According to this calculation, what was the largest improvement in run value from 2016 a 2017?

• 63.66
• 60.93
• 62.41
• 61.26

Settimana 5: Moneyball and Beyond Coursera Quiz Answers

Quiz 1: Settimana 5 – Quiz 1

Q1. Which two seasons have the strongest correlation between run values?

• 2016 & 2017
• 2015 & 2016
• 2014 & 2015
• 2014 & 2016

Q2. Which event has the highest sum of squares value?

• Batter Interference
• Catcher Interference
• Sac Fly DP
• Triple Play

Q3. What was the average run value of a “Flyout” in 2014?

• -0.2409
• -0.2292
• -0.2625
• -0.2479

Quiz 2: Settimana 5 – Quiz 2

Q1. What was the correlation in player run values between 2014 e 2016?

• 0.5101
• 0.4266
• 0.5461
• 0.4663

Q2. What is the R-squared for the regression model run in step 4?

• 0.301
• 0.308
• 0.303
• 0.305

Q3. What is the regression coefficient of RV15 when used as an independent variable in the regression?

• 0.0472
• 0.2673
• 0.3509
• 0.062

Quiz 3: Settimana 5 – Quiz 3

Q1. What was the correlation in team-run values between 2014 e 2017?

• 0.0652
• 0.2618
• 0.1931
• 0.3571

Q2. What is the R-squared for the regression model run in step 5?

• 0.127
• 0.123
• 0.130
• 0.118

Q3. What is the regression coefficient of RV16 when used as an independent variable in the regression?

• -0.4437
• 0.3788
• 0.0706
• -0.0553

Q4. Which independent variable(S) had coefficients that were significant in the player-level regression but insignificant in the team-level regression (al .05 significance level)?

• RV15
• RV15 & RV16
• RV16
• RV14 & RV15