# Status of XC- Analysis project

This project analyzes cross country team data with the hope of discovering knowledge about Coaching techniques, Peaking, Performance, Conditioning and Course difficulty. So far I have amassed data on about 20 teams, 150 runners, and about 75 races. But of course I need much more data. If you would like to help and send my your data click here.

I have plotted individual performances and team performances (see graphs on previous page) over a XC season (Sept to November).

Another way of looking at the data is - every runners performance on a scatter graph. Click here This graph was done using Gnuplot. Each point is a runners performance (Purdy pts) on a given day this fall. One notices immediately that most points fall on certain days. That is because the teams I have, mostly race on Wednesdays and Saturdays. A quick fix is to "jitter" the data. That is, since it is not too important if a performance occurs within a few days, we can randomly change the the day by + or - up to 4 days. Click here This better shows the overall trend in the season. Notice that there are fewer points near the end of the season. (especially the low performance points). This is because many Junior Varsity runners do not attend the last two championship meets. (Some coaches did not send the girls to the State class meets.)

# Analysis of XC -data using Xmdvtool

Xmdvtool is an X-windows program for analysing and viewing multi- dimensional data. Several displays have been created using this program with some results annotated.

Click here This display has X-country race performance data on 1307 individual performances. It has 5 dimensions ( day of the season- jittered, Performance, team # , Age in years, and amount of summer training in total miles. This first display uses scatter plots of all possible pairings of data. Points in red are values from just one team. Annotation 1 is a plot of Day VS Team, where team # is the X axis and Day is the Y. Each team's data comes out as a vertical stripe with team #2 in red. Gaps in 1 are weeks where there were no races, or some teams only had a few races. Other interesting points in this display are:

```                2 - There is only one College team hence the ages
on the team are all greater.....
3 - The Summer training (in total miles) was only available
for a few runners.. (most values are 0.. solid green
on bottom) . However the College team has the summer
training.
4 - Notice that the selected team has a wide range of
performances over the season. Also notice that in this
plot the best performances aren't really at the end of
the season (on the right of the plot)
5 - Again note the missing data (all 0's ) for Age and
summer training. Also note that for the selected team
all ages are known, and are in the range of 3 values
(15,16,17) soph,jr., Senior. This particular team
rarely  allows freshman to run Varsity and JV.
Other teams have runners age 14 (green vertical line)
6 - When trying to see correlations with top performances
(2nd row) there is one with high summer training. The
Selected team has some runners that had high summer
training values.

```