Predict NFL Touchdowns - Create Your First Predictive Model in Python (Step by Step Tutorial)

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NOTEBOOK LINK: colab.research.google.com/dri...
GITHUB LINK: github.com/nickwan/nfl-qb-td-...
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Nick Wan is the Director of Analytics for the Cincinnati Reds. He streams data science on Twitch and reacts to the latest news, sports, memes and everything in between.
In this video, Nick teaches you how to make an NFL analytics predictive model that predicts touchdowns. Follow along this walkthrough on Github/Google Collab!
Watch the full vod here: / 1584884306
Join the Discord: / discord
Twitter: / nickwan
WATCH LIVE ON TWITCH:
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Music Licensed by Soundstripe
Video Editor: Tony Pelleriti

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Опубликовано:

30 окт 2022

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Комментарии : 28

@tweakyapollo 5 месяцев назад

I am a 35 year old intern starting a career in analytics. Thank you for this

@Rapha_Carpio 5 месяцев назад

36 y/old here

@ak22196 3 месяца назад

@@Rapha_Carpio hey good luck to you both. How did you find internships are older appliers?

@peterk6215 Год назад

Thanks Nick. This was great! Hope that you do more in the future. 🙂

@h00die49 Год назад

Love this type of content, thanks Nick!

@WingfanSports Месяц назад

This was Herbert second season so a big jump not surprising. Adding some sort of age curve to this might also be smart

@daniellepashayan773 Месяц назад

@4:01 - create a list of the seasons you want to include and do a nested list comprehension using any. this helps too so if you ever want to add more seasons, you just edit the variable value instead of changing the code in the list comp desired_seasons = ['2019', '2020', '2021'] target_seasons = [season for season in seasons if any(target in season for target in desired_seasons )]

@averagejoe5822 Год назад

Nice! Love the content

@storiebrand4682 4 месяца назад

Explained very well thanks bro

@WingfanSports Месяц назад

Great video. For anyone that cares Matt Stafford no only switched teams but also played in the super bowl that year giving him 4 additional games. This model would be significantly better if you limited it to regular season only and maybe strip out players who switched teams

@longieboy 11 месяцев назад

Very nice! Could you bring more videos on prediction sport outcomes with python?

@briankkrieger1685 11 месяцев назад

I'm a complete noob, but I'm trying to learn. Do you have a video or instructions on how you download the github data so you can use it when following along in google collab

@Rapha_Carpio 5 месяцев назад

13:56 maybe you can get rid of all nan values.... just dropping it or fill those NaN with the mean..

@briankkrieger1685 11 месяцев назад

Also is there a resource for you to learn what pandas, matplotlib, and seaborn do for you?

@jeremyespinoza279 11 месяцев назад

Could you make a NFL fantasy one.

@tweakyapollo 5 месяцев назад

Does this only work with csv? Or can this be an Excel sheet

@Rapha_Carpio 5 месяцев назад

25:42 Can we like put colors to the top 5 Preds so if we are like looking to "buy" a new quarterback we can go for the top 5... I don't think you need all the 20 seasons, usually a player play at the top level 5-7 seasons..

@smasher7144 Год назад

I'm having some trouble with the loop and append section is there any other documentation to follow? Using VSCode for reference

@zachwells2842 11 месяцев назад

Same

@tyler_russell 9 месяцев назад

If you have more data, why wouldn’t you train with it? For example, why not 2019 and 2020 to train and then test on 2021.

@Hurtchie 9 месяцев назад

did it work?

@Rapha_Carpio 5 месяцев назад

15:02 Previous interceptions has nothing to do with the "next year touchdowns" because, the interceptions might be fault of the offensive player that did not catch the ball... or did not read the play very well and the defend just got the ball on the go... for newbies... we see that the correlation is so weak in that graphs "touchdowns and interceptions_prev" the line blue is not so high.