CodeHer Presentation

Editor's Notes

• #5: Cayla I am Cayla Evans. I am a junior at Bishop Ireton HS and am a national bound hockey player. I do not know what I want to do yet. I am planning to use the next two years to do that. This project was a way for me to see if tech is something I want to do. Dagny Joined husband in March to run our software & data integration consulting company Prior to that worked in across dotcom, telecom, data analytics industries –worked at several small growing DC business on cutting edges of industry Big believer there are many paths to tech
• #6: Cayla We decided to do this particular project because I am starting to think about what I want to study in College. Data Science seems cool. This project allows me to learn about Data Science using a topic I’m interested in. The real goal is to see if Data Science is something I want to do when I get out of college. Dagny Inspiration comes from many sources – this project is product of letting my mind wander I really wanted a project that would expose Cayla to technical opportunities, not just softer business skills (although we worked on those too) Husband too busy, so I leverage something I was good at
• #7: Cayla Used many different sources. My mother bought me a couple of books for understand concepts and even made me write book reportscon them. Also used various websites when I couldn’t figure out to do something and to find my data For majority of project used R.
• #8: Cayla I located the player and team stats of the ‘10-’11 through ‘14-’15 seasons. I took those stats & loaded them all into R so that I could correlate any of the stats with each other. Just a few days after the analysis, I realized that the stats I had loaded were not up to date. I was able to find and load new player/team stats. Right after the data was loaded and proved to be right I mapped out the plan for the rest of the project. Cleansing the data isn’t finished one time through I merged the player and goalie stats into the Rosters of all 30 teams in the NHL. Using the rosters I then calculated the averages for the player stats and the one goalie stat that would be needed to make the team stats. Once I calculated the averages I filled in the ‘blank’ 2015-2016 stats. I then aggregated or added the player and goalie statistics to make the team stats. Dagny My role – advisor, researcher, quality control, cardboard batman *applied model & correlation to both data sets
• #9: Cayla Important stats – shots, icorsi, ifenwick, Sv% Different approaches to get to the same results
• #10: (last 50 years) Shot on goal a flawed statistic because “on goal” – if it hits the goalie, it’s considered on goal. But if it hits the pipe, it’s not a shot. Goalie stat only not a player stat. still trying to figure out Take an example: Alex Ovechkin shoots – 1) goes in -> goal and shot; 2) 5 ft wide, but goalie grabs it -> shot; 3) 5 ft wide, but goalie doesn’t touch it -> no shot; 4) Hits the post, misses the net -> no shot Fenwick is shots plus all shot attempts that missed the net (i.e. hit the post/crossbar, shot wide, etc.) Corsi is Fenwick plus all shots attempts that were blocked by the defending team I have played hockey for the past 8 years. The best team does not always win. We are human. Humans have bad days. Since one player is not responsible for the win of a game the performance of the team is critical. Bad days for the players could mean a bad day for the team.
• #11: Example of 3 core player stats at team level. No clear outliers President’s cup winner (best team at end of regular season) did not win stanley cup Neither cup winners had significantly higher stats
• #12: The root is always the question I’m trying to answer – business question Mapped project from data collection to answering the business question Data collection; cleansing; analysis; results
• #15: One practical one: R is a bit finicky. It’s caching the work until you save it, so if you didn’t save enough or “reset the cache”, syntax that worked previously would return funky results