NCSU WOLFPACK 2012-2013 Men’s Basketball
During the 2012-2013 season so far, the NC State basketball team has been much more successful at home than away. • NC State has only lost one game at home this season. • NC State has won 70% of their games so far and has lost 30% of their games. What does the data tell us?
There is a positive correlation between the amount of time an NC State player gets to play and the average number of points he scores. • In general, the more time a player spends in the game, the more points he scores. • The data indicates that CJ Leslie, Richard Howell, and TJ Warren are pretty good offensively because their plot is above the line of best fit. • The data also indicates that Scott Wood, Rodney Purvis, and Tyler Lewis may not be as good offensively because their plot is below the line of best fit. What does the data tell us?
The slope of the line of best fit is approximately 0.4. This means that on average, for every minute a NC State basketball player is in the game, they score 0.4 of a point. More reasonably, you could say that they score an average of 2 points for every 5 minutes they play. • The correlation coefficient is 0.969, which means that there is a VERY strong correlation between playing time and scoring. • While the correlation is strong, there is no causation. Playing time is not a cause of scoring. What does the data tell us?
The histogram is probably not the best representation of the free throw percentage ranges of the team, because it includes the players who have not taken any free throws this season. • All players who have taken free throws this season have made at least half of them. • There are only 2 players who make at least 80% of their free throws. What does the data tell us?
Interquartile Range: 19 pts. Standard Deviation: 13.08 Mean: 78 Total Points Scored
2013 Standard Deviation 13.08 2012 Standard Deviation 9.5
There has been a greater spread of scores during the 2013 season as compared to the 2012 season. • In general, scoring has been higher during the 2013 season with a median in the 80s compared to a median in the 70s the year before. This is also true because the upper quartile in 2013 was much closer than the upper quartile in 2012. What does the data tell us?