AoCMM 2017 Results & Statistics

The third AoCMM competition hosted 124 teams from 21 different nations and regions across five major continents. We extend our congratulations to all competitors for their hard work! This year we published an article on MMA FOCUS , reaching out to more students with interest in math modeling

The score rubric this year is slightly different from that of last year. We took out graphical support, strength and weakness analysis, and sensitivity and stability support. These points are incorporated into other solution sections and a new section called Analysis and Assessment. By doing so, we emphasized the importance of the method and its explanation, rather than the analysis of method. It also gives more freedom of what participants should include in their paper.  You can download the new rubric here: 



 
 
 

The ages of the participants lowered from that of the previous years, demonstrating AoCMM's effort on targeting the underrepresented students.

Download the Rubric 

Like last year, each team are given 2 weeks to complete 2 problems listed below: 


  It has been shown typing patterns can be used to identify a person. To confirm this idea, we collected typing patterns from eleven of our officers using two different typing methods: fourteen short English quotes and six paragraphs composed of random characters. The quotes and paragraphs are grouped into three categories: eight quotes and three paragraphs where the officer’s identity is known, six quotes where the officer’s identity is unknown, and another three paragraphs where the officer’s identity is also unknown. Based on the first category, how would one match the second and third categories to the officers? ​​

The data can be downloaded at: 
https://drive.google.com/open?id=0B7h3lxLzcvgCdzVXbF9Tb0pab0k
https://www.dropbox.com/s/o5cqqitjib6niy5/AoCMM-Typing.zip?dl=0

These data were generated using https://www.keyhero.com/.

To type the random characters, click on typing practice -> typing lessons -> option 5) letters asdfqwerjkl;uiop.

Note that person K typed a different quote for test 1 from the rest of the ten people. The third category was only collected from ten of the eleven officers. You may try generating more typing data if necessary. Any information available on the website can be used towards your analysis.


 
 
2  There will always be times when taxis are vacant. Some drivers say that you should head to the city center to find more customers, but is that always true? 1) Suppose you are a taxi driver in NYC, what should you do when your car is vacant? 2) If you are the head of a taxi company, what would you advise your drivers do?

You may find taxi trip record data for Green Taxi in NYC at:
https://data.cityofnewyork.us/Transportation/2016-Green-Taxi-Trip-Data/hvrh-b6nb.

If you are unable to download the entire file, we have compiled a smaller version of the first 500,000 rows which can be downloaded at:
https://drive.google.com/open?id=0B7h3lxLzcvgCOEx6OS1CTVdnRFE or https://www.dropbox.com/s/hdc1xwd0dn450i8/2016_Green_Taxi_Trip_Data.xlsx?dl=0.

More information about this file can be found at:
https://drive.google.com/open?id=0B7h3lxLzcvgCWE5oUmZMclFtNWs or https://www.dropbox.com/s/gp0ogk6j24vgehj/data_dictionary_trip_records_green.pdf?dl=0.

Overall, this year's papers demonstrated significant improvement from past years'. Teams analyzed the problems more comprehensively, and they were able to assess their model more thoroghly compared with teams from past years. 

The greatest weakness appeared among all papers was to explain the model. In one case the team calculated the values before writing out the formula they used to calculate them, and in another case the team used assumptions that were not stated and justified in the beginning. While validating the model is important, the mathematical model itself is the core of the paper, and teams should devote more time to explain how they came to this model, and what they did in each step clearly, than to assess their model. 

Outstanding Teams

  • Grand Prize - Team 743 (Harvard-Westlake School, Mercyhurst Preparatory
                                  School, YK Pao School, The Hotchkiss School, USA)
  •                            - Ziyue(Sebastian) Li, Anran(Lyle) Huang, Shutian(Choco) Fang, 
  •                               and Zhurui(Jerry) Sheng
  •  
  • Alpha Prize - Team 692 (Pui Ching Middle School, Hong Kong)
  •                            - Cheung Pui Sang Joshua, Yuen Lok Kan, Tong Nok To Omega,
  •                              and Tsang Lok Kan Ethan

  • Alpha Prize - Team 723 (The High School Affiliated To Renmin University,
  •                               Hangzhou Foreign Languages School, The Madeira School, Jinhua
  •                               NO.1 High School, China)
  •                            - Yufei Pei, Tuxun Lu, Ruijia Ge, and Yuxin Jin
  •  
  • Beta Prize   -  Team 693 (Pui Ching Middle School, Hong Kong)
  •                           - Xu Wen Qi, Tam Chi Hang, Lau Tin Wai, and Chan Tsz Hin
  •  
    Beta Prize   -  Team 725 (Rabun Gap-Nacoochee School, USA)
                              - Yijie Hao
  •  
  • Beta Prize   - Team 726 (The Doon School, India)
  •                           - Shreyas Minocha, Arjun Agarwal, and Aditya Garg
  •  
  • Beta Prize   - Team 734 (Concordia University, Canada)
  •                           - Floriane Miquet
 
        Beta Prize   - Team 741 (Guangdong Experimental High School, China)
                                  - Jiahan Liu, and Shuyan Liu

  •  
  • Beta Prize   - Team 744 (The Pennington School, USA)
  •                           - Ye Teng, Xinyi Zheng, Zheng Bao, and Yiren Zhou

              Gamma  - Teams 622, 650, 652, 680, 682, 690, and 706

 
Prize Details
       Type                             - Percentile 
Grand Prize                    - top 1%            
Alpha Prize                     - top 3%  
Beta Prize                        - top 8%           
Gamma Prize                 - top 15%
Honorable Mentions - top 50%

Participants from around the world...

As AoCMM continues to grow, students learn about us from a variety of sources...