Web Scraping to Item Response Theory: A College Football Adventure
Brandon LeBeau, Andrew Zieffler, and Kyle Nickodem
University of Iowa & University of Minnesota
Background
- Began after Tim Brewster was fired
- Wanted to try to predict next great coach
Data Available
- Data is available at three levels
- Coach
- Game by Game
- Team
Coach
- Data
- Overall record
- Team history
- Not Available
- Coordinator history
Example Coach Data
## Year Team Win Loss Tie Pct PF PA Delta coach
## 1 2010 Iowa 8 5 0 0.61538 376 221 155 Kirk Ferentz
## 2 2011 Iowa 7 6 0 0.53846 358 310 48 Kirk Ferentz
## 3 2012 Iowa 4 8 0 0.33333 232 275 -43 Kirk Ferentz
## 4 2013 Iowa 8 5 0 0.61538 342 246 96 Kirk Ferentz
## 5 2014 Iowa 7 6 0 0.53846 367 333 34 Kirk Ferentz
Game by Game
- Data
- Final score of each game
- Date played
- Location
- Not Available
- No information within a game
Example GBG Data
## Team Official Year Date WL Opponent PF PA
## 1 Iowa University of Iowa 2014 8/30/2014 W Northern Iowa 31 23
## 2 Iowa University of Iowa 2014 9/6/2014 W Ball St. (IN) 17 13
## 3 Iowa University of Iowa 2014 9/13/2014 L Iowa St. 17 20
## 4 Iowa University of Iowa 2014 9/20/2014 W Pittsburgh (PA) 24 20
## 5 Iowa University of Iowa 2014 9/27/2014 W Purdue (IN) 24 10
## 6 Iowa University of Iowa 2014 10/11/2014 W Indiana 45 29
## 7 Iowa University of Iowa 2014 10/18/2014 L Maryland 31 38
## 8 Iowa University of Iowa 2014 11/1/2014 W Northwestern (IL) 48 7
## 9 Iowa University of Iowa 2014 11/8/2014 L Minnesota 14 51
## 10 Iowa University of Iowa 2014 11/15/2014 W Illinois 30 14
## 11 Iowa University of Iowa 2014 11/22/2014 L Wisconsin 24 26
## 12 Iowa University of Iowa 2014 11/28/2014 L Nebraska 34 37
## 13 Iowa University of Iowa 2014 1/2/2015 L Tennessee 28 45
## Location
## 1 Iowa City, IA
## 2 Iowa City, IA
## 3 Iowa City, IA
## 4 Pittsburgh, PA
## 5 West Lafayette, IN
## 6 Iowa City, IA
## 7 College Park, MD
## 8 Iowa City, IA
## 9 Minneapolis, MN
## 10 Champaign, IL
## 11 Iowa City, IA
## 12 Iowa City, IA
## 13 Jacksonville, FL
Team
- Data
- Overall team record
- Team statistics
- Rankings
- Conference Affiliation
- Data is very similar to that of the coach level
Web Scraping
- Data were obtained from many sources
- Much from http://cfbdatawarehouse.com
- Also used wikipedia, ESPN, and rivals
Iowa Coaches Over Time
Iowa State Coaches Over Time
Strengths in web scraping
- Data is relatively easily obtained
- Structured process for obtaining data
- Can be easily updated
Challenges of web scraping
- At the mercy of the website
- Many sites are old
- Not up to date on current design standards
- Data validation can be difficult and time consuming
- Need some basic knowledge of html
When is Web Scraping Worthwhile?
- Best when scraping many pages
- Particularly when web addresses are not structured
- Useful when data need to be updated
- Not useful if only scraping a single page/table
HTML Basics
- HTML is structured by start tags (e.g.
<table>) and end tags (e.g.<⁄table>) - Common tags
<h1>-<h6><b><i><a href="http://www.google.com"><table>
<p><ul>&<li><div><img>
- Highly structured pages are the easiest to scrape
HTML Code Example
Tools for web scraping
- R
- Python
beautiful soup: http://www.crummy.com/software/BeautifulSoup/
- Misc
SelectorGadget: http://selectorgadget.com/
Basics of rvest
read_htmlis the most basic functionhtml_nodeorhtml_nodes- These functions need css selectors or xpath
- SelectorGadget is the easiest way to get this
SelectorGadget
- SelectorGadget is a Javascript addon for web browsers
- Can quickly identify a css selector or xpath to select correct portion of web page
- Demo:
Combine SelectorGadget with rvest
library(rvest)
wiki_kirk <- read_html("https://en.wikipedia.org/wiki/Kirk_Ferentz")
wiki_kirk_extract <- wiki_kirk %>%
html_nodes(".vcard td , .vcard th")
head(wiki_kirk_extract)
## {xml_nodeset (6)}
## [1] <td colspan="2" style="text-align:center"><a href="/wiki/File:Kirk_p ...
## [2] <th scope="row">Sport(s)</th>
## [3] <td class="category">\n <a href="/wiki/American_football" title="Am ...
## [4] <th colspan="2" style="text-align:center;background-color: lightgray ...
## [5] <th scope="row">Title</th>
## [6] <td>\n <a href="/wiki/Head_coach" title="Head coach">Head coach</a> ...
Extract text
- Use the
html_textfunction
wiki_kirk_extract <- wiki_kirk %>%
html_nodes(".vcard td , .vcard th") %>%
html_text()
head(wiki_kirk_extract)
## [1] "\nFerentz at the 2010 Orange Bowl\n"
## [2] "Sport(s)"
## [3] "Football"
## [4] "Current position"
## [5] "Title"
## [6] "Head coach"
Encoding problems
- Two solutions to fix encoding problems
guess_encodingrepair_encoding: fix encoding problems
wiki_kirk %>%
html_nodes(".vcard td , .vcard th") %>%
html_text() %>%
guess_encoding()
## encoding language confidence
## 1 UTF-8 1.00
## 2 windows-1252 en 0.36
## 3 windows-1250 ro 0.18
## 4 windows-1254 tr 0.13
## 5 UTF-16BE 0.10
## 6 UTF-16LE 0.10
Fix Encoding Problems
- Best practice to reload page with correct encoding
wiki_kirk <- read_html("https://en.wikipedia.org/wiki/Kirk_Ferentz",
encoding = 'UTF-8')
- Can also repair encoding after the fact
wiki_kirk_extract <- wiki_kirk %>%
html_nodes(".vcard td , .vcard th") %>%
html_text() %>%
repair_encoding()
Extract html tags
- Use the
html_namefunction
wiki_kirk_extract <- wiki_kirk %>%
html_nodes(".vcard td , .vcard th") %>%
html_name()
head(wiki_kirk_extract)
## [1] "td" "th" "td" "th" "th" "td"
Extract html attributes
- Use the
html_attrsfunction
wiki_kirk_extract <- wiki_kirk %>%
html_nodes(".vcard td , .vcard th") %>%
html_attrs()
head(wiki_kirk_extract)
## [[1]]
## colspan style
## "2" "text-align:center"
##
## [[2]]
## scope
## "row"
##
## [[3]]
## class
## "category"
##
## [[4]]
## colspan
## "2"
## style
## "text-align:center;background-color: lightgray;"
##
## [[5]]
## scope
## "row"
##
## [[6]]
## named character(0)
Extract links
- Use the
html_attrsfunction again
wiki_kirk_extract <- wiki_kirk %>%
html_nodes(".vcard a") %>%
html_attr('href')
head(wiki_kirk_extract)
## [1] "/wiki/File:Kirk_pressconference_orangebowl2010.JPG"
## [2] "/wiki/American_football"
## [3] "/wiki/Head_coach"
## [4] "/wiki/Iowa_Hawkeyes_football"
## [5] "/wiki/Big_Ten_Conference"
## [6] "/wiki/Iowa_City,_Iowa"
Valid Links
- The
paste0function is helpful for this
valid_links <- paste0('https://www.wikipedia.org', wiki_kirk_extract)
head(valid_links)
## [1] "https://www.wikipedia.org/wiki/File:Kirk_pressconference_orangebowl2010.JPG"
## [2] "https://www.wikipedia.org/wiki/American_football"
## [3] "https://www.wikipedia.org/wiki/Head_coach"
## [4] "https://www.wikipedia.org/wiki/Iowa_Hawkeyes_football"
## [5] "https://www.wikipedia.org/wiki/Big_Ten_Conference"
## [6] "https://www.wikipedia.org/wiki/Iowa_City,_Iowa"
Extract Tables
- The
html_tablefunction is useful to scrape well formatted tables
record_kirk <- wiki_kirk %>%
html_nodes(".wikitable") %>%
.[[1]] %>%
html_table(fill = TRUE)
Caveats to Web Scraping
- Keep in mind when scraping we are using their bandwidth
- Do not want to repeatedly do expensive bandwidth operations
- Better to scrape once, then run only to update data
- Some websites are copyrighted (i.e. illegal to scrape)
Data Modeling
- Research Questions
- Who is the next great coach?
- What characteristics are in common for these coaches?
IRT modeling
- So far we have explored the win/loss records of teams in the BCS era with item response theory (IRT)
- IRT is commonly used to model assessment data to estimate item parameters and person 'ability'
- We recode the Win/Loss/Tie game by game results
- 1 = Win
- 0 = Otherwise
Example code with lme4
- A 1 parameter multilevel IRT model can be fitted using
glmerin thelme4package
library(lme4)
fm1a <- glmer(wingbg ~ 0 + (1|coach) + (1|Team),
data = yby_coach, family = binomial)
Plot Showing Team Ability
Connect
- e-mail: brandon-lebeau (at) uiowa.edu
- Twitter: @blebeau11; https://twitter.com/blebeau11
- Linkedin: https://www.linkedin.com/in/lebeaubr
- Website: http://brandonlebeau.org
Web Scraping to Item Response Theory: A College Football Adventure
Brandon LeBeau, Andrew Zieffler, and Kyle Nickodem
University of Iowa & University of Minnesota