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Interview Prof Benjamin Alamar , Sports Analytics
Here is an interview with Prof Benjamin Alamar, founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA.
Ajay – The movie Moneyball recently sparked out mainstream interest in analytics in sports.Describe the role of analytics in sports management
Benjamin- A very typical first step for a team is to utilize the tools of predictive analytics to help inform their draft decisions.
Benjamin- I got involved in sports through a company called Protrade Sports. Protrade initially was a fantasy sports company that was looking to develop a fantasy game based on advanced sports statistics and utilize a stock market concept instead of traditional drafting. I was hired due to my background in economics to develop the market aspect of the game.
There I met Roland Beech (who now works for the Mavericks) and Aaron Schatz (owner of footballoutsiders.com) and learned about the developing field of sports statistics. I then changed my research focus from economics to sports statistics and founded the Journal of Quantitative Analysis in Sports. Through the journal and my published research, I was able to establish a reputation of doing quality, useable work.
For students, I recommend developing very strong data management skills (sql and the like) and thinking carefully about what sort of questions a general manager or coach would care about. Being able to demonstrate analytic skills around actionable research will generally attract the attention of pro teams.
About-
Benjamin Alamar, Professor of Sport Management, Menlo College
Professor Benjamin Alamar is the founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He has published academic research in football, basketball and baseball, has presented at numerous conferences on sports analytics. He is also a co-creator of ESPN’s Total Quarterback Rating and a regular contributor to the Wall Street Journal. He has consulted for teams in the NBA and NFL, provided statistical analysis for author Michael Lewis for his recent book The Blind Side, and worked with numerous startup companies in the field of sports analytics. Professor Alamar is also an award winning economist who has worked academically and professionally in intellectual property valuation, public finance and public health. He received his PhD in economics from the University of California at Santa Barbara in 2001.
Prof Alamar is a speaker at Predictive Analytics World, San Fransisco and is doing a workshop there
http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#day2-17
2:55-3:15pm
Track 1: Sports Analytics
Case Study: NFL, MLB, & NBA
Competing & Winning with Sports Analytics
The field of sports analytics ties together the tools of data management, predictive modeling and information systems to provide sports organization a competitive advantage. The field is rapidly developing based on new and expanded data sources, greater recognition of the value, and past success of a variety of sports organizations. Teams in the NFL, MLB, NBA, as well as other organizations have found a competitive edge with the application of sports analytics. The future of sports analytics can be seen through drawing on these past successes and the developments of new tools.
You can know more about Prof Alamar at his blog http://analyticfootball.blogspot.in/ or journal at http://www.degruyter.com/view/j/jqas. His detailed background can be seen at http://menlo.academia.edu/BenjaminAlamar/CurriculumVitae
Interview JJ Allaire Founder, RStudio
Here is an interview with JJ Allaire, founder of RStudio. RStudio is the IDE that has overtaken other IDE within the R Community in terms of ease of usage. On the eve of their latest product launch, JJ talks to DecisionStats on RStudio and more.
Ajay- So what is new in the latest version of RStudio and how exactly is it useful for people?
JJ- The initial release of RStudio as well as the two follow-up releases we did last year were focused on the core elements of using R: editing and running code, getting help, and managing files, history, workspaces, plots, and packages. In the meantime users have also been asking for some bigger features that would improve the overall work-flow of doing analysis with R. In this release (v0.95) we focused on three of these features:
Projects. R developers tend to have several (and often dozens) of working contexts associated with different clients, analyses, data sets, etc. RStudio projects make it easy to keep these contexts well separated (with distinct R sessions, working directories, environments, command histories, and active source documents), switch quickly between project contexts, and even work with multiple projects at once (using multiple running versions of RStudio).
Version Control. The benefits of using version control for collaboration are well known, but we also believe that solo data analysis can achieve significant productivity gains by using version control (this discussion on Stack Overflow talks about why). In this release we introduced integrated support for the two most popular open-source version control systems: Git and Subversion. This includes changelist management, file diffing, and browsing of project history, all right from within RStudio.
Code Navigation. When you look at how programmers work a surprisingly large amount of time is spent simply navigating from one context to another. Modern programming environments for general purpose languages like C++ and Java solve this problem using various forms of code navigation, and in this release we’ve brought these capabilities to R. The two main features here are the ability to type the name of any file or function in your project and go immediately to it; and the ability to navigate to the definition of any function under your cursor (including the definition of functions within packages) using a keystroke (F2) or mouse gesture (Ctrl+Click).
Ajay- What’s the product road map for RStudio? When can we expect the IDE to turn into a full fledged GUI?
JJ- Linus Torvalds has said that “Linux is evolution, not intelligent design.” RStudio tries to operate on a similar principle—the world of statistical computing is too deep, diverse, and ever-changing for any one person or vendor to map out in advance what is most important. So, our internal process is to ship a new release every few months, listen to what people are doing with the product (and hope to do with it), and then start from scratch again making the improvements that are considered most important.
Right now some of the things which seem to be top of mind for users are improved support for authoring and reproducible research, various editor enhancements including code folding, and debugging tools.
What you’ll see is us do in a given release is to work on a combination of frequently requested features, smaller improvements to usability and work-flow, bug fixes, and finally architectural changes required to support current or future feature requirements.
While we do try to base what we work on as closely as possible on direct user-feedback, we also adhere to some core principles concerning the overall philosophy and direction of the product. So for example the answer to the question about the IDE turning into a full-fledged GUI is: never. We believe that textual representations of computations provide fundamental advantages in transparency, reproducibility, collaboration, and re-usability. We believe that writing code is simply the right way to do complex technical work, so we’ll always look for ways to make coding better, faster, and easier rather than try to eliminate coding altogether.
Ajay -Describe your journey in science from a high school student to your present work in R. I noticed you have been very successful in making software products that have been mostly proprietary products or sold to companies.
Why did you get into open source products with RStudio? What are your plans for monetizing RStudio further down the line?
JJ- In high school and college my principal areas of study were Political Science and Economics. I also had a very strong parallel interest in both computing and quantitative analysis. My first job out of college was as a financial analyst at a government agency. The tools I used in that job were SAS and Excel. I had a dim notion that there must be a better way to marry computation and data analysis than those tools, but of course no concept of what this would look like.
From there I went more in the direction of general purpose computing, starting a couple of companies where I worked principally on programming languages and authoring tools for the Web. These companies produced proprietary software, which at the time (between 1995 and 2005) was a workable model because it allowed us to build the revenue required to fund development and to promote and distribute the software to a wider audience.
By 2005 it was however becoming clear that proprietary software would ultimately be overtaken by open source software in nearly all domains. The cost of development had shrunken dramatically thanks to both the availability of high-quality open source languages and tools as well as the scale of global collaboration possible on open source projects. The cost of promoting and distributing software had also collapsed thanks to efficiency of both distribution and information diffusion on the Web.
When I heard about R and learned more about it, I become very excited and inspired by what the project had accomplished. A group of extremely talented and dedicated users had created the software they needed for their work and then shared the fruits of that work with everyone. R was a platform that everyone could rally around because it worked so well, was extensible in all the right ways, and most importantly was free (as in speech) so users could depend upon it as a long-term foundation for their work.
So I started RStudio with the aim of making useful contributions to the R community. We started with building an IDE because it seemed like a first-rate development environment for R that was both powerful and easy to use was an unmet need. Being aware that many other companies had built successful businesses around open-source software, we were also convinced that we could make RStudio available under a free and open-source license (the AGPLv3) while still creating a viable business. At this point RStudio is exclusively focused on creating the best IDE for R that we can. As the core product gets where it needs to be over the next couple of years we’ll then also begin to sell other products and services related to R and RStudio.
About-
JJ Allaire
JJ Allaire is a software engineer and entrepreneur who has created a wide variety of products including ColdFusion,Windows Live Writer, Lose It!, and RStudio.
From http://en.wikipedia.org/wiki/Joseph_J._Allaire
In 1995 Joseph J. (JJ) Allaire co-founded Allaire Corporation with his brother Jeremy Allaire, creating the web development tool ColdFusion.[1] In March 2001, Allaire was sold to Macromedia where ColdFusion was integrated into the Macromedia MX product line. Macromedia was subsequently acquired by Adobe Systems, which continues to develop and market ColdFusion.
After the sale of his company, Allaire became frustrated at the difficulty of keeping track of research he was doing using Google. To address this problem, he co-founded Onfolio in 2004 with Adam Berrey, former Allaire co-founder and VP of Marketing at Macromedia.
On March 8, 2006, Onfolio was acquired by Microsoft where many of the features of the original product are being incorporated into the Windows Live Toolbar. On August 13, 2006, Microsoft released the public beta of a new desktop blogging client called Windows Live Writer that was created by Allaire’s team at Microsoft.
Starting in 2009, Allaire has been developing a web-based interface to the widely used R technical computing environment. A beta version of RStudio was publicly released on February 28, 2011.
JJ Allaire received his B.A. from Macalester College (St. Paul, MN) in 1991.
RStudio-
RStudio is an integrated development environment (IDE) for R which works with the standard version of R available from CRAN. Like R, RStudio is available under a free software license. RStudio is designed to be as straightforward and intuitive as possible to provide a friendly environment for new and experienced R users alike. RStudio is also a company, and they plan to sell services (support, training, consulting, hosting) related to the open-source software they distribute.
SOPA RIP
- Any effort to combat online piracy must guard against the risk of online censorship of lawful activity and must not inhibit innovation by our dynamic businesses large and small (AJ-yup)
- We must avoid creating new cybersecurity risks or disrupting the underlying architecture of the Internet. (AJ-note this may include peer-to-peer browsers, browser extensions for re-routing and newer forms of encryption, or even relocation of internet routers in newer geographies )
We must avoid legislation that drives users to dangerous, unreliable DNS servers and puts next-generation security policies, such as the deployment of DNSSEC, at risk.
While we are strongly committed to the vigorous enforcement of intellectual property rights, existing tools are not strong enough to root out the worst online pirates beyond our borders.
We should never let criminals hide behind a hollow embrace of legitimate American values
and
We should all be committed to working with all interested constituencies to develop new legal tools to protect global intellectual property rights without jeopardizing the openness of the Internet. Our hope is that you will bring enthusiasm and know-how to this important challenge
Authored by
Victoria Espinel is Intellectual Property Enforcement Coordinator at Office of Management and Budget
————————————————————————–
AJ-Why not sponser a hackathon, White House and create a monetary incentive for hackers to suggest secure ways? Atleast a secure dialogue between policy makers and policy breakers could be a way forward.
SOPA in its current form is dead. We live to fight another day.
—————————————————————————–
Quote-
Let us never negotiate out of fear. But let us never fear to negotiate. John F K
Does the Internet need its own version of credit bureaus
Data Miners love data. The more data they have the better model they can build. Consumers do not love data so much and find sharing data generally a cumbersome task. They need to be incentivize for filling out survey forms , and for signing to loyalty programs. Lawyers, and privacy advocates love to use examples of improper data collection and usage as the harbinger of an ominous scenario. George Orwell’s 1984 never “mentioned” anything about Big Brother trying to sell you one more loan, credit card or product.
Data generated by customers is now growing without their needing to fill out forms and surveys. This data is about their preferences , tastes and choices and is growing in size and depth because it is generated from social media channels on the Internet.It is this data that can be and is captured by social media analytics.
Mobile data is also growing, including usage of location based applications and usage of Internet from the mobile phone is leading to further increases in data about consumers.Increasingly , location based applications help to provide a much more relevant context to the data generated. Just mobile data is expected to grow to 15 exabytes by 2015.
People want to have more and more conversations online publicly , share pictures , activity and interact with a large number of people whom they have never met. But resent that information being used or abused without their knowledge.
Also the Internet is increasingly being consolidated into a few players like Microsoft, Amazon, Google and Facebook, who are unable to agree on agreements to share that data between themselves. Interestingly you can use Yahoo as a data middleman between Google and Facebook.
At the same time, more and more purchases are being done online by customers and Internet advertising has grown much above the rate of growth of other mediums of communication.
Internet retail sales have the advantage that better demand predictability can lead to lower inventories as retailers need not stock up displays to look good. An Amazon warehouse need not keep material to simply stock up it shelves like a K-Mart does.
Our Hypothesis – An Analogy with how Financial Data Marketing is managed offline
- Financial information regarding spending and saving is much more sensitive yet the presence of credit bureaus alleviates these concerns.
- Credit bureaus collect information from all sources, aggregate and anonymize the individual components accordingly.They use SSN as a unique identifier.
- The Internet has a unique number too , called the Internet Protocol Address (I.P)
- Should there be a unique identifier like Internet Security Number for the Internet to ensure adequate balance between the need for privacy as well as the need for appropriate targeting?
After all, no one complains about privacy intrusions if their credit bureau data is aggregated , rolled up, and anonymized and turned into a propensity model for sending them direct mailers.
Advertising using Social Media and Internet
https://www.facebook.com/about/ads/#stories
1. A business creates an adLet’s say a gym opens in your neighborhood. The owner creates an ad to get people to come in for a free workout.
2. Facebook gets paid to deliver the adThe owner sends the ad to Facebook and describes who should see it: people who live nearby and like running.
The right people see the ad3. Facebook only shows you the ad if you live in town and like to run. That’s how advertisers reach you without knowing who you are.
Adding in credit bureau data and legislative regulation for anonymizing and handling privacy data can expand the internet selling market, which is much more efficient from a supply chain perspective than the offline display and shop models.
Privacy Regulations on Marketing using Internet data
Should laws on opt out and do not mail, do not call, lists be extended to do not show ads , do not collect information on social media. In the offline world, you can choose to be part of direct marketing or opt out of direct marketing by enrolling yourself in various do not solicit lists. On the internet the only option from advertisements is to use the Adblock plugin if you are Google Chrome or Firefox browser user. Even Facebook gives you many more ads than you need to see.
One reason for so many ads on the Internet is lack of central anonymize data repositories for giving high quality data to these marketing companies.Software that can be used for social media analytics is already available off the shelf.
The growth of the Internet has helped carved out a big industry for Internet web analytics so it is a matter of time before social media analytics becomes a multi billion dollar business as well. What new developments would be unleashed in this brave new world is just a matter of time, and of course of the social media data!
Using Google Fusion Tables from #rstats
But after all that- I was quite happy to see Google Fusion Tables within Google Docs. Databases as a service ? Not quite but still quite good, and lets see how it goes.
https://www.google.com/fusiontables/DataSource?dsrcid=implicit&hl=en_US&pli=1
http://googlesystem.blogspot.com/2011/09/fusion-tables-new-google-docs-app.html
But what interests me more is
http://code.google.com/apis/fusiontables/docs/developers_guide.html
The Google Fusion Tables API is a set of statements that you can use to search for and retrieve Google Fusion Tables data, insert new data, update existing data, and delete data. The API statements are sent to the Google Fusion Tables server using HTTP GET requests (for queries) and POST requests (for inserts, updates, and deletes) from a Web client application. The API is language agnostic: you can write your program in any language you prefer, as long as it provides some way to embed the API calls in HTTP requests.
The Google Fusion Tables API does not provide the mechanism for submitting the GET and POST requests. Typically, you will use an existing code library that provides such functionality; for example, the code libraries that have been developed for the Google GData API. You can also write your own code to implement GET and POST requests.
Also see http://code.google.com/apis/fusiontables/docs/sample_code.html
Google Fusion Tables API Sample Code
Libraries
SQL API
| Language | Library | Public repository | Samples |
|---|---|---|---|
| Python | Fusion Tables Python Client Library | fusion-tables-client-python/ | Samples |
| PHP | Fusion Tables PHP Client Library | fusion-tables-client-php/ | Samples |
Featured Samples
An easy way to learn how to use an API can be to look at sample code. The table above provides links to some basic samples for each of the languages shown. This section highlights particularly interesting samples for the Fusion Tables API.
SQL API
| Language | Featured samples | API version |
|---|---|---|
| cURL |
|
SQL API |
| Google Apps Script |
|
SQL API |
| Java |
|
SQL API |
| Python |
|
Docs List API |
| Android (Java) |
|
SQL API |
| JavaScript – FusionTablesLayer | Using the FusionTablesLayer, you can display data on a Google Map
Also check out FusionTablesLayer Builder, which generates all the code necessary to include a Google Map with a Fusion Table Layer on your own website. |
FusionTablesLayer, Google Maps API |
| JavaScript – Google Chart Tools | Using the Google Chart Tools, you can request data from Fusion Tables to use in visualizations or to display directly in an HTML page. Note: responses are limited to 500 rows of data. | Google Chart Tools |
External Resources
Google Fusion Tables is dedicated to providing code examples that illustrate typical uses, best practices, and really cool tricks. If you do something with the Google Fusion Tables API that you think would be interesting to others, please contact us at googletables-feedback@google.com about adding your code to our Examples page.
- Shape EscapeA tool for uploading shape files to Fusion Tables.
- GDALOGR Simple Feature Library has incorporated Fusion Tables as a supported format.
- Arc2CloudArc2Earth has included support for upload to Fusion Tables via Arc2Cloud.
- Java and Google App EngineODK Aggregate is an AppEngine application by the Open Data Kit team, uses Google Fusion Tables to store survey data that is collected through input forms on Android mobile phones. Notable code:
- Create a Fusion Table - FusionTableServlet.java
- Insert data into a Fusion Table - SubmissionFusionTable.java
- R packageAndrei Lopatenko has written an R interface to Fusion Tables so Fusion Tables can be used as the data store for R.
- RubySimon Tokumine has written a Ruby gem for access to Fusion Tables from Ruby.
Updated-You can use Google Fusion Tables from within R from http://andrei.lopatenko.com/rstat/fusion-tables.R
ft.connect <- function(username, password) {
url = "https://www.google.com/accounts/ClientLogin";
params = list(Email = username, Passwd = password, accountType="GOOGLE", service= "fusiontables", source = "R_client_API")
connection = postForm(uri = url, .params = params)
if (length(grep("error", connection, ignore.case = TRUE))) {
stop("The wrong username or password")
return ("")
}
authn = strsplit(connection, "\nAuth=")[[c(1,2)]]
auth = strsplit(authn, "\n")[[c(1,1)]]
return (auth)
}
ft.disconnect <- function(connection) {
}
ft.executestatement <- function(auth, statement) {
url = "http://tables.googlelabs.com/api/query"
params = list( sql = statement)
connection.string = paste("GoogleLogin auth=", auth, sep="")
opts = list( httpheader = c("Authorization" = connection.string))
result = postForm(uri = url, .params = params, .opts = opts)
if (length(grep("<HTML>\n<HEAD>\n<TITLE>Parse error", result, ignore.case = TRUE))) {
stop(paste("incorrect sql statement:", statement))
}
return (result)
}
ft.showtables <- function(auth) {
url = "http://tables.googlelabs.com/api/query"
params = list( sql = "SHOW TABLES")
connection.string = paste("GoogleLogin auth=", auth, sep="")
opts = list( httpheader = c("Authorization" = connection.string))
result = getForm(uri = url, .params = params, .opts = opts)
tables = strsplit(result, "\n")
tableid = c()
tablename = c()
for (i in 2:length(tables[[1]])) {
str = tables[[c(1,i)]]
tnames = strsplit(str,",")
tableid[i-1] = tnames[[c(1,1)]]
tablename[i-1] = tnames[[c(1,2)]]
}
tables = data.frame( ids = tableid, names = tablename)
return (tables)
}
ft.describetablebyid <- function(auth, tid) {
url = "http://tables.googlelabs.com/api/query"
params = list( sql = paste("DESCRIBE", tid))
connection.string = paste("GoogleLogin auth=", auth, sep="")
opts = list( httpheader = c("Authorization" = connection.string))
result = getForm(uri = url, .params = params, .opts = opts)
columns = strsplit(result,"\n")
colid = c()
colname = c()
coltype = c()
for (i in 2:length(columns[[1]])) {
str = columns[[c(1,i)]]
cnames = strsplit(str,",")
colid[i-1] = cnames[[c(1,1)]]
colname[i-1] = cnames[[c(1,2)]]
coltype[i-1] = cnames[[c(1,3)]]
}
cols = data.frame(ids = colid, names = colname, types = coltype)
return (cols)
}
ft.describetable <- function (auth, table_name) {
table_id = ft.idfromtablename(auth, table_name)
result = ft.describetablebyid(auth, table_id)
return (result)
}
ft.idfromtablename <- function(auth, table_name) {
tables = ft.showtables(auth)
tableid = tables$ids[tables$names == table_name]
return (tableid)
}
ft.importdata <- function(auth, table_name) {
tableid = ft.idfromtablename(auth, table_name)
columns = ft.describetablebyid(auth, tableid)
column_spec = ""
for (i in 1:length(columns)) {
column_spec = paste(column_spec, columns[i, 2])
if (i < length(columns)) {
column_spec = paste(column_spec, ",", sep="")
}
}
mdata = matrix(columns$names,
nrow = 1, ncol = length(columns),
dimnames(list(c("dummy"), columns$names)), byrow=TRUE)
select = paste("SELECT", column_spec)
select = paste(select, "FROM")
select = paste(select, tableid)
result = ft.executestatement(auth, select)
numcols = length(columns)
rows = strsplit(result, "\n")
for (i in 3:length(rows[[1]])) {
row = strsplit(rows[[c(1,i)]], ",")
mdata = rbind(mdata, row[[1]])
}
output.frame = data.frame(mdata[2:length(mdata[,1]), 1])
for (i in 2:ncol(mdata)) {
output.frame = cbind(output.frame, mdata[2:length(mdata[,i]),i])
}
colnames(output.frame) = columns$names
return (output.frame)
}
quote_value <- function(value, to_quote = FALSE, quote = "'") {
ret_value = ""
if (to_quote) {
ret_value = paste(quote, paste(value, quote, sep=""), sep="")
} else {
ret_value = value
}
return (ret_value)
}
converttostring <- function(arr, separator = ", ", column_types) {
con_string = ""
for (i in 1:(length(arr) - 1)) {
value = quote_value(arr[i], column_types[i] != "number")
con_string = paste(con_string, value)
con_string = paste(con_string, separator, sep="")
}
if (length(arr) >= 1) {
value = quote_value(arr[length(arr)], column_types[length(arr)] != "NUMBER")
con_string = paste(con_string, value)
}
}
ft.exportdata <- function(auth, input_frame, table_name, create_table) {
if (create_table) {
create.table = "CREATE TABLE "
create.table = paste(create.table, table_name)
create.table = paste(create.table, "(")
cnames = colnames(input_frame)
for (columnname in cnames) {
create.table = paste(create.table, columnname)
create.table = paste(create.table, ":string", sep="")
if (columnname != cnames[length(cnames)]){
create.table = paste(create.table, ",", sep="")
}
}
create.table = paste(create.table, ")")
result = ft.executestatement(auth, create.table)
}
if (length(input_frame[,1]) > 0) {
tableid = ft.idfromtablename(auth, table_name)
columns = ft.describetablebyid(auth, tableid)
column_spec = ""
for (i in 1:length(columns$names)) {
column_spec = paste(column_spec, columns[i, 2])
if (i < length(columns$names)) {
column_spec = paste(column_spec, ",", sep="")
}
}
insert_prefix = "INSERT INTO "
insert_prefix = paste(insert_prefix, tableid)
insert_prefix = paste(insert_prefix, "(")
insert_prefix = paste(insert_prefix, column_spec)
insert_prefix = paste(insert_prefix, ") values (")
insert_suffix = ");"
insert_sql_big = ""
for (i in 1:length(input_frame[,1])) {
data = unlist(input_frame[i,])
values = converttostring(data, column_types = columns$types)
insert_sql = paste(insert_prefix, values)
insert_sql = paste(insert_sql, insert_suffix) ;
insert_sql_big = paste(insert_sql_big, insert_sql)
if (i %% 500 == 0) {
ft.executestatement(auth, insert_sql_big)
insert_sql_big = ""
}
}
ft.executestatement(auth, insert_sql_big)
}
}
Interview Mike Boyarski Jaspersoft
Here is an interview with Mike Boyarski , Director Product Marketing at Jaspersoft
.
the largest BI community with over 14 million downloads, nearly 230,000 registered members, representing over 175,000 production deployments, 14,000 customers, across 100 countries.
How to Measure and Optimize Your Marketing:Tableau
Tired of everyone calling everyone else a social media expert. What seperates the social media men from the fakes.
the grain from the chaff.
Data.
Heres a webcast you may want to think of social media tickles your curiosity on how to leverage these strange new marketing tools.
https://cc.readytalk.com/cc/schedule/display.do?udc=xu40u1ex8tj8
Notice the form and fields below the screenshot-
is every field required – is each one required to measure
Have they optimized web registration forms yet.
