So this is a table with summary data for each company. As mentioned, we make an assumption that every next company is better than the previous. We call going from company A to company B a "win" for company B, and a "loss" for company A. Note that these companies doesn't have to be from neighbouring co-op terms - for a student with 5 work terms, the fifth company will get 4 "wins" (one "win" for each of the previous companies, assuming they are distinct). That's how you get "win", "loss" and "win-loss balance" stats. Note that having too much losses doesn't necessarily mean that copany is bad - it usually means that students did internships at this company during their early work terms. If the student worked at comapny A, then B and then A again, for the ranking purposes we only consider the last occurence of each company. Only companies with more than one student were considered.

Once we got the win/losses concept established, let's rank the companies. If we treat companies as teams and wins/losses (see definition above) as the result of a game between them, you can come up with company rankings. Unfortunately ELO rankings are highly dependent on the order of the games, so TrueSkill will be used for rankings (this algorithm is used by Microsoft for XBOX). Note that the rankings aren't perfect - TrueSkill penalizes heavily for uncertainty, so a cool startup which only had 5 students working for it is very unlikely to make it to the top. Or Google got penalized way too heavily. You might get better insights by examining what students at the company of your interest ended up doing.

Only companies with at least 3 different students interning for them are presented here. "Avg. internships per student" refers to how many internships students did at the particular company, provided that they did at least one. This is a good estimator to whether students are willing to return to that place.

The table entries are sortable - if you want to sort companies, say, by Win-Loss balance, click on Win-Loss.