Posts from February 2019 (17)

February 8, 2019

Super Rugby Predictions for Round 1

Team Ratings for Round 1

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Current Rating Rating at Season Start Difference
Crusaders 17.67 17.67 0.00
Hurricanes 9.43 9.43 0.00
Chiefs 8.56 8.56 0.00
Lions 8.28 8.28 -0.00
Highlanders 4.01 4.01 0.00
Waratahs 2.00 2.00 -0.00
Sharks 0.45 0.45 0.00
Brumbies 0.00 0.00 -0.00
Jaguares -0.26 -0.26 0.00
Stormers -0.39 -0.39 0.00
Blues -3.42 -3.42 0.00
Bulls -3.79 -3.79 0.00
Rebels -7.26 -7.26 -0.00
Reds -8.19 -8.19 -0.00
Sunwolves -16.08 -16.08 -0.00

 

Predictions for Round 1

Here are the predictions for Round 1. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Game Date Winner Prediction
1 Chiefs vs. Highlanders Feb 15 Chiefs 8.00
2 Brumbies vs. Rebels Feb 15 Brumbies 10.80
3 Blues vs. Crusaders Feb 16 Crusaders -17.60
4 Waratahs vs. Hurricanes Feb 16 Hurricanes -3.40
5 Sunwolves vs. Sharks Feb 16 Sharks -12.50
6 Bulls vs. Stormers Feb 16 Bulls 0.10
7 Jaguares vs. Lions Feb 16 Lions -4.50

 

Rugby Premiership Predictions for Round 13

Team Ratings for Round 13

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Current Rating Rating at Season Start Difference
Saracens 10.90 11.19 -0.30
Exeter Chiefs 10.76 11.13 -0.40
Wasps 3.85 8.30 -4.40
Northampton Saints 3.46 3.42 0.00
Leicester Tigers 3.28 6.26 -3.00
Harlequins 3.07 2.05 1.00
Gloucester Rugby 2.44 1.23 1.20
Sale Sharks 2.00 -0.81 2.80
Bath Rugby 1.88 3.11 -1.20
Worcester Warriors -2.71 -5.18 2.50
Bristol -3.51 -5.60 2.10
Newcastle Falcons -3.83 -3.51 -0.30

 

Performance So Far

So far there have been 72 matches played, 52 of which were correctly predicted, a success rate of 72.2%.
Here are the predictions for last week’s games.

Game Date Score Prediction Correct
1 Sale Sharks vs. Saracens Jan 04 24 – 18 -4.50 FALSE
2 Exeter Chiefs vs. Bristol Jan 05 14 – 9 21.20 TRUE
3 Leicester Tigers vs. Gloucester Rugby Jan 05 34 – 16 5.10 TRUE
4 Newcastle Falcons vs. Harlequins Jan 05 17 – 38 0.40 FALSE
5 Worcester Warriors vs. Bath Rugby Jan 05 21 – 19 0.70 TRUE
6 Wasps vs. Northampton Saints Jan 06 27 – 16 4.80 TRUE

 

Predictions for Round 13

Here are the predictions for Round 13. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Game Date Winner Prediction
1 Bath Rugby vs. Newcastle Falcons Feb 16 Bath Rugby 11.20
2 Bristol vs. Wasps Feb 16 Wasps -1.90
3 Gloucester Rugby vs. Exeter Chiefs Feb 16 Exeter Chiefs -2.80
4 Harlequins vs. Worcester Warriors Feb 16 Harlequins 11.30
5 Northampton Saints vs. Sale Sharks Feb 16 Northampton Saints 7.00
6 Saracens vs. Leicester Tigers Feb 16 Saracens 13.10

 

Pro14 Predictions for Round 15

 

Team Ratings for Round 15

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

 

Current Rating Rating at Season Start Difference
Leinster 12.65 9.80 2.90
Munster 9.86 8.08 1.80
Glasgow Warriors 7.30 8.55 -1.20
Scarlets 3.31 6.39 -3.10
Connacht 2.70 0.01 2.70
Ospreys 1.30 -0.86 2.20
Cardiff Blues 0.57 0.24 0.30
Edinburgh 0.17 -0.64 0.80
Ulster -0.70 2.07 -2.80
Cheetahs -1.96 -0.83 -1.10
Treviso -2.79 -5.19 2.40
Dragons -8.03 -8.59 0.60
Southern Kings -9.87 -7.91 -2.00
Zebre -13.96 -10.57 -3.40

 

Performance So Far

So far there have been 98 matches played, 77 of which were correctly predicted, a success rate of 78.6%.
Here are the predictions for last week’s games.

 

Game Date Score Prediction Correct
1 Cheetahs vs. Southern Kings Feb 03 40 – 36 13.30 TRUE

 

Predictions for Round 15

Here are the predictions for Round 15. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

 

Game Date Winner Prediction
1 Ospreys vs. Ulster Feb 16 Ospreys 6.50
2 Edinburgh vs. Dragons Feb 16 Edinburgh 12.70
3 Munster vs. Southern Kings Feb 16 Munster 24.20
4 Zebre vs. Leinster Feb 17 Leinster -22.10
5 Treviso vs. Scarlets Feb 17 Scarlets -1.60
6 Connacht vs. Cheetahs Feb 17 Connacht 9.20
7 Cardiff Blues vs. Glasgow Warriors Feb 17 Glasgow Warriors -2.20

 

Meet Summer Scholar Larisa Morales Soto

Every summer, the Department of Statistics offers scholarships to high-achieving students so they can work with staff on real-world projects. Larisa Morales Soto, below, is working with Dr Beatrix Jones on a project exploring how the dietary patterns of New Zealand children change during their childhood and the transition to adolescence. 

Unlike most of the Department of Statistics’ summer scholars, Larisa isn’t studying locally. Summer scholarship are open to anyone tertiary student who is appropriately qualified, and Larisa, who is in her third year studying genomic science at the National Autonomous University of Mexico at Morelos in south central Mexico, leapt at the chance to gain new experiences overseas.

“What first motivated my search is that this time of the year would be winter in the northern hemisphere, and research internship programs are not very common in this season,” says Larisa, whose study combines biology, computer science and statistics to answer questions in life sciences.

“What finally brought me to New Zealand was the high academic quality and international presence of The University of Auckland. Also, the summer programme would give me the chance to visit the country and get completely immersed in the culture, something I wouldn’t have been able to do without the scholarship.”

The work she is doing looks at how the overall dietary patterns of New Zealand kids change during their childhood and the transition to adolescence. “During early life stages, children’s parents determine their food intake,” she explains, “but as they grow up, they start making decisions on which foods they want to eat, and their previous diet and other external factors can influence this decision-making process.”

The research hopes to shed light on the complex relationship between diet, health and disease during an individual’s lifespan, understanding how different factors help to establish dietary patterns.

Being in New Zealand has brought Larisa personal and academic benefits: “This experience is having a huge impact on my professional training. But also, I feel that it’s making me grow on the personal level as well, because being alone and very far from my home country is a big step. Being here has changed the perspective I had of New Zealand – I’ve been able to see the greatness of the country in terms of natural resources, social culture, economy and politics.”

In her down time, Larisa has been using the university sports and recreation centre and the library, as well as visiting parks, museums and other attractions in the city and Hauraki Gulf. She also fitted in a quick trip to the South Island with a friend she made here.

This won’t be the last we see of her, says Larisa: “I am definitely coming back in the future.”

  • For general information on University of Auckland summer scholarships, click here.

 

 

February 7, 2019

Meet Summer Scholar Yongshi Deng

Every summer, the Department of Statistics offers scholarships to high-achieving students so they can work with staff on real-world projects. Yongshi Deng, below, is working with communications company Vodafone New Zealand on a project analysing and predicting customer behaviour. 

Yongshi, who has a BSc in Mathematics and Statistics and an Honours in Statistics from the University of Auckland, is spending seven weeks working within Vodafone’s Big Data and Analytics team.

Her research focuses on analysing and modelling opt-out behaviour among Vodafone’s fixed line customers – those who have purchased broadband, landline telephone and television services. She is using a combination of data on what customers do when they use these services, network data and information from Vodafone’s call centre.

It’s a big job, Yongshi says, but she is relishing it: “I enjoy this project a lot, as I can put my knowledge I learn from my degrees into practice.” There are plenty of problems to solve: “The major challenge is data cleaning, since big, real-world datasets can be very messy. There are millions of missing values that need to be handled.”

Another challenge is variable selection. “The dataset I am currently working on has more than 120 variables, so this makes dimension reduction indispensable,” Yongshi explains. It’s critical that she chooses a good combination of variables to build models that can generalise well for unseen data. This step, she says, is based not only on statistical tests but also on domain knowledge, which she gets from her colleagues at Vodafone.

Yongshi’s supervisor at Vodafone, Neel Sengupta, says that having students in-house brings benefits to both parties. “They get to see what business data looks like and the sheer scale of it. The benefit for us is that we get to see the advancements coming out of universities that we might not necessarily see in a commercial set-up.”

Yongshi’s supervisor in the Department of Statistics, Ciprian Giurcaneanu, agrees that the biggest benefit to students of such work experience is that ”they get in contact with the real world. This allows them to see how useful in practice are the techniques that they have learned in our department.” They also have to fend for themselves: “The lecturer “who knows everything” is not there, and the students have to find their own answers to their questions.”

Yongshi is originally from Dongguan, China. This year, she plans to pursue a PhD at the University of Auckland, and already has a good idea of the field she wants to research: “I am particularly interested in applying machine learning techniques to solve real-world problems.”

Yongshi says that statistics is a “fantastic subject” that not only helps her explore the world, but keeps her motivated and engaged. She particularly appreciates the R programming skills that she has learned in the Department of Statistics. “The department provides a wide range of statistical courses and R is integrated in most courses, which has equipped me with the skill and knowledge I’ll need for further study.”

  • For general information on University of Auckland summer scholarships, click here.

 

February 2, 2019

Meet Simon Goodwin, Statistics summer scholar

Every summer, the Department of Statistics offers scholarships to high-achieving students so they can work with staff on real-world projects. Simon Goodwin, below, is working with Dr Jesse Goodman on random graph dynamics and hitting times.

Simon’s summer scholarship is related to the study of random graphs, looking at how to investigate networks that look as if they are random or pseudo-random, like social networks, family trees or the global flight network.  His task in particular is looking at the nodes in these structures that are hard to reach by moving randomly, and what this means for the structure of the graph as a whole.

You can conceptualise it like this: Produce a random graph by connecting pairs of vertices uniformly at random. Then run a random walk on this random graph: at each step, move to a uniformly chosen neighbour of the current position. The hitting time is the number of steps needed to reach a particular target vertex, and it varies in a particular way depending on the size of the random graph.

Simon’s work looks at the effect of changing the random graph. Between each random walk step, he might “rewire” some edges: pick a fraction of edges, disconnect the vertices on either side, then randomly reconnect those vertices to see if these graph dynamics make it faster (or slower) to reach the target vertex.

“Looking at the structure of these random theoretical objects we can learn about vast real-world networks that have no clearly apparent structure,” Simon explains. “The results I am trying to find would also have theoretical applications in the study of random graphs.”

Simon is about to start his third year studying maths and statistics: “My main interest is in pure maths, but I am also very interested in theoretical statistics, mainly in probability. I am intrigued by all things random.”

In fact, he dropped physics for statistics last year, “and I haven’t regretted it for one moment – sorry physics! I am mainly interested in probability but I have also enjoyed learning about data analysis and I have an interest in statistical computing.”

He adds, “Probability is such an interesting field, as it has a strong theoretical backing while also having many obvious applications such as games with dice and cards, as well as many less obvious applications, from financial-market analysis to quantum physics.”

Simon is hoping to become an academic: “I hope to continue into postgraduate study and then spend the rest of my life studying and teaching what I love.” When he’s not studying, Simon loves playing video games and roleplaying games like Dungeons and Dragons, as well as walking around the scenic spots of Auckland.

  • For general information  on University of Auckland summer scholarships, click here.
February 1, 2019

Meet Yiwen He, Statistics Summer Scholar

Every summer, the Department of Statistics offers scholarships to high-achieving students so they can work with staff on real-world projects. Yiwen, below, is working with Professor Chris Wild on iNZight, the free data visualisation and analysis software he developed.

Yiwen is doing a conjoint BSc and BCom majoring in Statistics, Mathematics and Finance at the University of Auckland. She’s from China, and moved here seven years ago.

Yiwen is working on the Department of Statistics-based data analysis package iNZight.

This is a free, R-based environment started by statistics education expert Professor Chris Wild to help high-school students quickly and easily explore data and understand some statistical ideas.

However, iNZight has grown, and now extends to multivariable graphics, time series, and generalised linear modelling, including modelling of data from complex surveys. It is available in web and desktop versions.

As iNZight has expanded, it has needed tweaking and tidying, and Yiwen is working on how it copes with incoming data that has date and time fields telling us when something happened. “These data are most likely to be in non-standard form, meaning our computing software cannot recognise and get useful information from it,” she explains.

Yiwen has been working with the iNZight team to develop functions to convert raw dates and times data to a standard format that iNZight can recognise, and extract desired components from a dates-and-times variable. “If we are able to automate how dates and times are handled by our computing software, we can plot dates and times together with our observations.”

Yiwen is finding the work stimulating and fun, “since we get to do things that are more practical, and it is exciting to see how the functions you build actually work on various data sets. And since we are given plenty of time in the project, it really encourages you to explore what is out there and extend your knowledge to more advanced coding stuff.”

High-achieving students like are a critical part of the development of iNZight, says Chris Wild. “It’s a student-driven project, so most of the big-scale changes occur over the New Zealand summer period. At other times, we mostly work on small changes and bug fixes.”

+ For general information  on University of Auckland summer scholarships, click here.