You can find complete instructions on how to create this table in “Measure Batting with Batting Average”. I used a table containing the total batting statistics for each player in each year (and the list of all teams for which each player played), and I called the table b_and_t. In this example, I wanted to look at the distribution of batting average. So, here’s our plan: we’ll put each player-season into a bucket, count the number of player-seasons in each bucket, and draw a graph showing (in ascending order) the number of players in each bucket. 354 average, so we’ll put that season in the. Instead, we might want to know the number of players with very similar batting averages-say, between. But we don’t really care about each unique value for example, the fact that 13 players had a batting average of. You can plot the number of players with each unique batting average (though I can’t imagine what this graph would look like).
Between 19, 6,032 players had qualifying batting averages, and there were 1,229 unique values for batting average. 400, or was there some pattern?īatting average can take many different values. What are the lowest and highest values? Are there more low values than high values? Were batting averages totally random numbers between 0 and. Let’s also assume you want to know how these values are distributed. Suppose you have a lot of data-say, the batting averages for all 6,032 baseball players between 19 who averaged 3.1 or more plate appearances per game. One of the most powerful tools available for understanding data is the histogram, a picture of the distribution of values.
We’re good at looking at a picture and observing different characteristics we’re bad at looking at a list of 1,000 numbers. There is some truth to the cliché “a picture is worth a thousand words.” A picture is often the best way to understand 1,000 numbers. Use Microsoft Excel to plot data distributions so that you can have a better understanding of statistics.
0 kommentar(er)
