IOTO

Overview

This project aims to use feature engineering to help track and evaluate parliamentary and legislative members. We will leverage public data and attempt to engineer new features which may enhance engagement and/or predictive and decision making analytics of legislative bodies.

Background

Expected Goals ($xG$) is an example of a number used to compare performance in the complex team activity of playing soccer. $xG$ indicates the probability of a soccer player’s shot resulting in a goal. $xG$ is used to rank players and their teams on ability to capitalize on chances (do they score more than $xG$ expected, or less?). $xG$ is an example of feature engineering a technique used to enhance predictive and decision-making in analytics. Analytics helps to explain, understand, and increase engagement in complex phenomena such as baseball and a broad range of other activities.

Challenge

Given data for one or more ’leagues’ of legislative player, derive at least one number like $xG$ that allows legislative players to be compared in a fair and transparent way that may enhance engagement and/or predictive and decision-making analytics of legislative ‘play.’ Numbers could be comparative – about legislative players and their topics. They could be about situating topics in some kind of spectrum or space in which players can be located and compared. Examine the data and discover what might work – you may be uncovering a dimensionless quantity that is implicitly defined!

Data

At a minimum, APIs covering topic data for various legislative leagues (Canada, BC, Alberta, etc.) will be made available to the M2PI team. These APIs reliably serve data concerning legislative ‘players’ and their topic-related interventions over a number of legislative sessions. Further datasets concerning elections (how many voted for the legislative player?), voting (what bills and their topics did a legislative player vote for/against) and financial data (what spending did the legislature approve and how is revenue generated?) may be made available – depending time available, which legislative leagues the M2PI team elects to study, and how they choose to analyse.

• Finance data are available from OECD, Statistics Canada, and legislative ’leagues’ themselves.

• Topics are standardized along Comparative Agendas Project (CAP) lines

• Charts of accounts for finance overlap topic categories, but do not correspond exactly.

• Elections data are available for all elected officials.

• Voting data for bills and motions may be available for certain legislatures.