Week 4 – @NFLFantasy PPR Play/Bench Using #MachineLearning

Week 4 – While it is 5am on Sunday and gameplay kicks off at 9:30am today (love those London games), I am technically getting this posted before the start. Ok, well, I missed Thursday – fair enough. But we are getting there:

League 1 (PPR):

Week4-@NFL-PPR-League1-@NFLFantasyAs I mentioned in my Week 3 post, there are some things that even the best algorithmic usage cannot predict (today); injuries are one of those things that today are sort of hard to determine unless player has a propensity or an existing / underlying issue that one is tracking – Very soon, I believe that the development of the RFID program that each player’s’ uniforms now carry will yield better and better data points that can, in fact, become the Minority Report of the NFL season and predict the next great injury, based on biometric + performance + known medical history with a fair degree of certainty. But we are not there yet (at least, what is being provided to those of us NOT connected to the team’s/NFL <– a girl can dream about this kind of data coming from this type of connection, can’t she?)

You’ll notice I have Jordan Reed in my line up – this is a hard one. He is expected to play but at what level? I picked up Charles Clay as my backup but unfortunately , Reed doesn’t play until Monday; Clay plays at 1pm today. So, I will refresh my model and then see if Reed drops any in the ranking. If nothing else, it will give me a better picture on points earned potential IF he were to play with his existing injury (albeit healed or otherwise).

I swapped out Doug Baldwin who is also questionable this week in terms of his health; and slotted Adam Thielen in. I also picked up Alvin Kamara off of the waiver wire. I have a feeling about him; as does my model. This week, he ranks right next to Terrance West given the matchup and alternates on my bench (oh woe is my RB situation) – Thank goodness for a stout WR lineup, even with injuries et al.

All in all, I am most worried about this matchup. This might be a week for a loss, even if numerically, it looks like a win on NFL.com. I guess I can thank Ty M. for making it a possibility. But O’dell B Jr. might have something else to say about that. Oh that and Melvin Gordon questionable status: if only it were so…Again, I love to dream…

League 2 (Standard): What made me thankful in League 1, has plagued me in League 2 (oh, Ty!) – In this league, I have already pulled out Jordan Reed because E. Engram ranked higher in my weekly ranking anyway. I am also taking a chance on Jarvis Landry this week (it is New Orleans after all. The point differential +/- standard deviation for Standard format sent Landry into the top 10 for the week – fingers crossed xx – #trustthemodel).

Oh yea, this is also the league where I have incurred 1 loss so far – 2 wins / 1 loss. 😦 And that loss was to none other than my man; we are competing in both League 1 and League 2; he beat me by 1.9 points Week 1 – somehow I don’t remember him feeling as bad for me with my loss that week as I did for his loss against me in Week 3…

In fact,  I think he did a victory dance perhaps akin to Tom Cruise in ‘Risky Business’ – but maybe that is too much information for a Sports ML blog posting :0):

Week4-Standard-@NFLFantasy-@NFL-@Laura_E_Edell

Week4-Standard-@NFLFantasy-@NFL-@Laura_E_Edell

League 3 (Standard): After Thursday night, I am doing ok – I predicted 12.5 +/- .75 st dev for Jordan Howard and he earned 12.30. So, pretty spot on. Aaron Rodgers came in at 23.06, which I predicted 23.60 (what, is that my dyslexia at play; no, there was also a        + / – 1.5 st dev at work, so again, spot on in terms of accuracy.  So, maybe, my 3 wins and 0 losses will become 4 wins after this week. But I am not counting any chickens ever before they are hatched. Just look at what happened to my opponent / my man in Week 3 (154 points for me; 45 points for him in PPR format- that is what we call, just brutal people). 

Week4-Standard-League3-@NFL-@NFLFantasy-@Laura_E_Edell

Week4-Standard-League3-@NFL-@NFLFantasy-@Laura_E_Edell

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Week 3 – @NFLFantasy PPR Play/Bench Using #MachineLearning

Recap from Week 3 (sorry – I really am trying to post before Thursday night but it seems that between work right now and updating my model stats mid-week, I just run out of time).

Week 3 was wildly successful. NFL.com was closer this time in terms of predicting my win over my opponent but nowhere near to the results that I achieved. I will always stand by Russell Wilson – what kind of Seahawk would I be if I threw in the towel and in my 2nd league (Standard format), he did not fail! He was simply divine. But alas, he is not my primary league QB (Tom Brady is – a hard pill to swallow personally being a die hard Seahawks fan after what happened in a certain very important yesteryear game – but he has proven his PPR fantasy value in Week 3). Primary League Week 3 – Wins = 3 / Losses = 0 (remember, after draft day, I was projected to end the season with an 8-8 W/L ratio. So, this might be the week; maybe not).

But last week, I genuinely felt bad – Locheness Jabberwokies, my week 3 opponent, happens to also be my man. And, this annihilation just felt like a win that went one step over the line of fairness. I mean a win’s a win – but this kind of decimation belongs outside of one’s relationship. Trust me. But he was a good sport. Except, he will no longer listen to my neurotic banter about losing in any given week, even if all signs point to a loss. Somehow, when I trust my model, it all works out. Now, I can’t predict injuries mid game like what happened in Week 4 to Ty Montgomery (my League 3 Flex position player). Standard league wise, he brought home 2.3 points ~ projected to earn about 10.70 Standard points with a st. deviation of +/- 1.5. But this was my lineup for Week 3 across my 3 leagues:

League #1 (Primary PPR) – remember, I aim to not just win but also optimize my lineup. #nfl.com,#fantasyfootball,#PPR,#Week3,#2017

A bench full of points is a fail to me. But in this case, I benched Jordan Reed and picked up whomever was the next available TE off the waiver wire (granted he definitely contributed nothing). But out of my WR1 and WR2 + WR Flex, those I played were the best options (even though Mike Evans came in about 1.10 points less than Adam Thielen (bench), it was within the expected standard deviation, so either one would have been fine if played).

My RB situation has always been the bane of my league this year starting with my draft choices – Nothing to write home about except seeing the early value of Kareem Hunt (TG), even when NFL.com continued to project very little in his court.

Terrance West was supposed to be double digits but my model said to bench him vs. either Mike Gillislee or Kerwynn Williams. Both scored very little and essentially were within their own standard deviation negating their slight point difference.

All in all, players played worked out well and yes, though many stellar performances carried those that failed might be outliers in some regard (or at least they won’t bring home that many points week over week).  But the PPR space is my golden circle of happiness – after all, I built my original algorithm using PPR league play / bench + historical point spreads + my secret sauce nearly 5 years ago; and those years of learning have “taught” the model (and me) many nuances otherwise missed by others in the sports ML space (though I respect greatly what my fellow ML “sportstaticians” put forth, my approach is very different from what I glean from others’ work).

One day, I would love to have a league with only ML Sports folks; the great battle of the algorithmic approaches – if you are interested, let me know in the comments.

League 2 (Standard): Wins = 3/Losses = 0:

As you can see, I should have played DeSean Jackson over Adam Thielen or my Flex position Ty Montgomery. And geez, I totally spaced on pulling Jordan Reed like I did in League 1. This win was largely because of Russell Wilson, as mentioned before, Devonta Freeman and the Defense waiver wire pick up of the Bengals who Im glad I picked up in time for the game. oh yeah, I am not sure why Cairo Santos shows as BYE but earned me 6 points??? NFL.com has some weird stuff happening around 12:30 last Sunday ; games showed as in play (even though kick off wasn’t for another 30 minutes); and those that showed in play erroneously allowed players to be added from the wire still as though the games weren’t kicked off. Anyway, not as proud but still another win – Year 1 for Standard; perhaps after another 5 years training Standard like my PPR league, I will have more predictable outcomes , other than luck.

#NFL.com, #Week3, #Standard,@NFLFantasy, #machinelearning

#NFL.com, #Week3, #Standard,@NFLFantasy

Win @NFLFantasy PPR Leagues w/ ML

So, the past 3 years I have been using #machine-learning (ML) to help me in my family based PPR #fantasy-football league. When I joined the league, the commissioner and my partner’s father, said I would never win using statistics as the basis of my game play. Being cut from the “I’ll show you” aka “well, fine…I’ll prove it to you” cloth that some of us gals working in the tech industry dawn as we break down stereotypical walls and glass ceilings, is something I’ve always enjoyed about my career and love that there is an infographic to tell the tale courtesy of mscareergirl.com (only complaint is the source text in white is basically impossible to read but at least the iconography and salaries are legible):

glass-ceiling-2-620x400

infographic provided by Danyel Surrency Jones on mscareergirl.com

I’ve never whined that in my industry, I tend to work primarily with the male species or that they are “apparently” paid more *on average* because some survey says so.  My work ethic doesn’t ride on gender lines — This train departs from the “proven value based on achievements earned & result in commensurate remuneration” station (woah, that’s a mouthful).  I take challenges head-on not to prove to others, but to prove to myself, that I can do something I set my sights on, and do that something as well, if not better, than counterparts. Period. Regardless of gender. And that track has led to the figurative money ‘line’ (or perhaps it’s literal in the case of DFS or trains – who’s to say? But I digress…more on that later).

Money_Train

So, I joined his league on NFL.com, so aptly nicknamed SassyDataMinxes (not sure who the other minxes are in my 1 woman “crew” but I never said I was grammarian; mathematician, aka number ninja, well, yes;  but lingualist, maybe not.

Year 1, as to be expected *or with hindsight*,  was an abysmal failure. Keep in mind that I knew almost absolute nothing when it came to Pro Football or Fantasy sports.  I certainly did not know players or strategies or that fantasy football extended beyond Yahoo Pick ‘Em leagues, which again, in hindsight, would have been a great place to start my learning before jumping head 1st into the world of PPR/DFS.

At its core, it requires you pick the weekly winning team from 2 different competitors and assuming you have the most correct picks, you win that week. If there are no teams on BYE that week, you have 32 teams or 16 games to “predict outcomes” ; a binary 0 or 1 for lose or win in essence. Right? Never, she exclaims, because WAIT, THERE’S MORE: you have to pick a winner based on another factor: point spread. Therefore, if 0 means lost and 1 means won, you get a 1 per win EXCEPT if the spread of points is less than what the “book makers” out of Vegas determine to be the “winning spread” – You could technically pick the winning team and still get a goose egg for that matchup if the team did not meet their point spread (ooh, it burns when that happens). The team that wins happily prancing around the field singing “We are the Champions” while you are the loser for not betting against them because they were comfortable winning by a paltry lesser amount than necessary – Ooh, the blood boils relieving those early games – especially since my Grandmother who won that year picked her teams based on cities she liked or jersey colors that her ‘Color Me Happy’ wheel said were HER best COOL tones; the most unscientific approach worked for her so many times I now think she actually IS A bookie running an illegal operation out of her basement, which fronts as ‘her knitting circle” – Yah, as if any of us believe that one, Grandma :)! (She is a walking football prediction algorithm).

So, something as seemingly simple as Yahoo Pick ‘Em can actually be harder than it appears unless you are her. But, still…markedly easier than a PPR league; and light years easier than DFS/Auction style fantasy leagues when it comes to predicting gaming outcomes at the player, weekly matchup and league perspectives.

Hindsight is such a beautiful thing (*I think I have said that before*) because to espouse all of these nuggets of knowledge as though I am the Alliteration Arbiter of All –> The Socratic Seer of Scoring Strategies…And again, as always I digress (but, ain’t it fun!).

OK, let’s continue…So, we’ve established that fantasy football gaming outcomes requires a lot of *something* — And we’ve established that just cuz it seems simple, or did, when trying to predict outcomes along a massively mutable set of variables  *wait, why didn’t I just READ that sentence or THINK it when I started! If I could go back in time and ask my 3 year younger:

“Self, should I stop this nonsense now, alter the destination or persevere through what, at times, might seems like a terrible journey? *HUM, I think most pensively*.

And then answer myself, just like the good only-child I am:

“NEVER – Self, nothing worth getting is easy to get, but the hardest fought wins are the most worthwhile when all is said and done and remember,  don’t let the bedbugs bite; YOU’RE bigger than them/that.”

Or something along those lines, perhaps…

NEVER was my answer because in 2015 and 2016 (Years 2 and 3),  I was #1 in the league and won those coveted NFL.com trophies and a small pool of money. But what I won most of all was bragging rites.

Champion

Oh baby, you can’t buy those…

Not even on the Dark Web from some Onion-Routed Darker Market. Especially the right to remind a certain commissioner / neighsayer du jour / father-in-law-like that my hypothesis of using ML & statistics ALONE could beat his years of institutional football knowledge and know-how. I also won a 2nd NFL-managed league that I joined in Year 3 to evaluate my own results with a different player composition.

So Year 1 was a learning year, a failure to others in the league but super valuable to me. Year 2 was my 1st real attempt to use the model, though with much supervision and human “tweaking” ; Year 3, non-family league (league #2 for brevity sake; snarky voice in head “missed that 4 paragraphs ago” – Burn!), I had drafted an ideal team, rated A- .

Year 3 being a double test to ensure Year 1 wasn’t a double fluke.  Two leagues played:

League #1 with the family, was a team comprised of many non-ideal draft picks chosen during non-optimized rounds (QB in round 2, DEF in round 4 etc). But the key in both Year 2 and 3, was spotting the diamonds in the rookie rough – my model bubbled up unknown players or as they are known to enthusiasts: “deep sleepers” that went on to become rookie-of-the-year type players: in 2015, that was Devonta Freeman (ATL); in 2016, that was ‘Ty-superfreak’ Hill aka Tyreek Hill (Chiefs) and even better, Travis Kelce (who had been on my roster since 2015 but rose to the occasion in 2016, BEEEG-TIME) .  That has always been a strength of my approach to solving this outcomes conundrum.

So, that all being said, in this Year 4, I plan to blog PRE-GAME with my predictions for my team with commentary on some of the rankings of other players. Remember, it isnt just a player outcome, but it is player outcome in relation to your matchup that week within your league and in the context of who best to play vs. bench given those weekly changing facets. Some weeks, you might look like a boss according to NFL.com predictions; but in fact, should be playing someone else who might have a lower-than-you-are-comfortable-putting-into-your-lineup prediction. Those predictions folks HAVE ISSUES – But I believe in the power of model evaluation and learning, hence the name Machine Learning or better yet, Deep Learning approaches.

Side note: reminds me of that “SAP powered” player comparison tool:

fantasyimageswhich was DOWN / not accessible most of the aforementioned season when it was being hammered on by fans in need of a fantasy fix (reminds me of an IBM Watson joke but I was keep that one to myself ) – whomever is at fault – you should make sure your cloud provider “models” out an appropriate growth-based capacity & utilization plan IF you are going to feature it on your fantasy football site, NFL.com.

Next posting will be all about how I failed during Year 4’s draft (2017) and what I am planning to do to make up for it using the nuggets of knowledge that is an offshoot of retraining the MODEL(s) during the week – Plus, I will blog my play/bench predictions which will hopefully secure a week 1 win (hopefully because I still need to retrain this week but not until Wednesday :)).

In a separate post, we’ll talk through the train…train…train phases, which datasets are most important to differentiate statistically important features from the sea of unworthy options sitting out waiting for you to pluck them into your world. But dont fall prey to those sinister foe…They might just be the “predictable” pattern of noise  that clouds one’s senses. And of course, scripting and more scripting; so many lines of code were written and rewritten covering the gamut of scripting languages from the OSS data science branch (no neg from my perspective on SaS or SPSS other than they cost $$$ and I was trained on R in college *for free* like most of my peers) – well, free is a relative term, and you take the good with the bad when you pull up your OSS work-boots –> R has its drawbacks when it comes to the viability of processing larger than life datasets without herculean sampling efforts just to be able to successfully execute a .R web scraping script without hitting the proverbial out of memory errors, or actually train the requisite models that are needed to solve said self-imposed ML fantasy football challenges such as this. Reader thinks to oneself, “she sure loves those tongue twisting alliterations.”

And gals, I love helping out a fellow chica (you too boys/men, but you already know that, eh) — Nobody puts baby in the corner, and I never turn my back on a mind in need or a good neg/dare.

Well, Year 4 — Happy Fantasy Football Everyone — May the wind take you through the playoffs and your scores take you all the way to the FF Superbowl 🙂

Futures According to Laura… Convergence of Cloud and Neural Networking with Mobility and Big Data

It’s been longer and longer between my posts and as always, life can be inferred as the reason for my delay.

But I was also struggling with feeling a sense of “what now” as it relates to Business Intelligence.

Many years ago, when I first started blogging, I would write about where I thought BI needed to move in order to remain relevant in the future. And those futures have come to fruition lately. Gamuts ranging from merging social networking datasets into traditional BI frameworks to a more common use case of applying composite visualizations to data (microcharts, as an example). Perhaps more esoteric was my staunch stance on the Mobile BI marriage which when iPhone 1 was released was a future many disputed with me. In fact, most did not own the first release of the iPhone, and many were still RIM subscribers. And it was hard for the Blackberry crowd to fathom a world unbounded by keyboards and scroll wheels and how that would be a game changer for mobile BI. And of course, once the iPad was introduced, it was a game over moment. Execs everywhere wanted their iPads to have the latest and greatest dashboards/KPIs/apps. From Angry Birds to their Daily Sales trend, CEOs and the like had new brain candy to distract them during those drawn out meetings. And instead of wanting that PDF or PowerPoint update, they wanted to receive the same data on their iPad. Once they did, they realized that having the “WHAT” is happening understanding was only the crack to get them hooked for a while. Unfortunately, the efficacy of KPI colors and related numbers only satisfies the one person show – but as we know, it isn’t the CEO who analyzes why a RED KPI indicator shows up. Thus, more levels of information (beyond the “WHAT” and  “HOW OFTEN”)  were needed to answer the “WHY” and “HOW TO FIX” the underlying / root cause issue.

The mobile app was born.

It is the reborn mobile dashboard that has been transformed into a new mobile workflow, more akin to the mobile app. 

But it took time for people to understand the marriage between BI dashboards, the mobile wave, especially the game change that Apple introduced with it’s swipe and pinch to zoom gestures, the revolution of the App Stores for the “need to have access to it now” generation of Execs, the capability to write-back from mobile devices to any number of source systems and how functionally, each of these seemingly unrelated functions would and could be weaved together to create the next generation of Mobile Apps for Business Intelligence. 

But that’s not what I wanted to write about today. It was a dream of the past that has come to fruition. 

Coming into 2013, cloud went from being something that very few understood to another game changer in terms of how CIOs are thinking about application support of the future. And that future is now.

But there are still limitations that we are bound by. Either we have a mobile device or not, either it is on 3 or 4G or wifi. Add to that our laptops (yes, something I believe will not dominate the business world in a future someday). And compound that with other devices like smartphones, eReaders, desktop computers et al. 

So, I started thinking about some of the latest research regarding Neural Networks (another set of posts I have made about the future of communication via Neural networks) published recently by Cornell University here (link points to http://arxiv.org/abs/1301.3605).

And my nature “plinko” thought process (before you ask, search for the Price is Right game and you will understand “Plinko Thoughts”) bounced from Neural Networks to Cloud Networks and from Cloud Networks to the idea of a Personal Cloud. 

A cloud of such personal nature that all of our unique devices are forever connected in our own personal sphere and all times when on our person. We walk around and we each have our own personal clouds. Instead of a mass world wide web, we have our own personal wide area network and our own personal wide web.

When we interact with other people, those people can choose to share their Personal networks with us via Neural Networking or some other sentient process, or in the example, where we bump into a friend and we want to share details with them, all of our devices have the capability to interlink to each other via our Personal Clouds. 

Devices are always connected to your Personal Cloud which is authenticated to your person, so that passwords which are already reaching their shelf life (see: article for more information on this point), are no longer the annoying constraint when we try to seamlessly use our mobile devices while on the go. Instead, they are authenticated to our Personal Cloud following similar principles as where IAM (Identity and Access Management) is moving towards in future. And changes in IAM are not only necessary for this idea to come to fruition but are on the horizon.

In fact, Gartner published an article in July 2012, called “Hype Cycle for Identity and Access Management Technologies, 2012” in which Gartner recognized that the growing adoption of mobile devices, cloud computing, social media and big data were converging to help drive significant changes in the identity and access management market.

For background purposes, IAM processes and technologies work across multiple systems to manage:

■ Multiple digital identities representing individual users, each comprising an identifier (name or key) and a set of data that represent attributes, preferences and traits

■ The relationship of those digital identities to each user’s civil identity

■ How digital user identities communicate or otherwise interact with those systems to handle
information or gain knowledge about the information contained in the systems

If you extrapolate that 3rd bullet out, and weave in what you might or might not know/understand about Neural Networking or brain-to-brain communication (see recent Duke findings by Dr. Miguel Nicolelis here) (BTW – the link points to http://www.nicolelislab.net/), one can start to fathom the world of our future. Add in cloud networking, big data, social data and mobility, and perhaps, the Personal Cloud concept I extol is not as far fetched as you initially thought when you read this post. Think about it.

My dream like with my other posts is to be able to refer back to this entry years from now with a sense of pride and “I told you so.” 

Come on – any blogger who makes predictions which come true years later deserves some bragging rites. 

Or at least, I think so…

To Start Quilting, One Just Needs a Set of Patterns: Deconstructing Neural Networks (my favorite topic de la journée, semaine ou année)

 

How a Neural Network Works:

Neural NetworkA neural network (#neuralnetwork) uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases.

This is not your mom’s apple pie or the good old days of case-based reasoning or fuzzy logic. (Although, the latter is still one of my favorite terms to say. Try it: fuzzzzyyyy logic. Rolls off the tongue, right?)…But I digress…

And, now, we’re back.

To give you a quick refresher:

image

Case based reasoning represents knowledge as a database of past cases and their solutions. The system uses a six-step process to generate solutions to new problems encountered by the user.

We’re talking old school, folks…Think to yourself, frustrating FAQ pages, where you type a question into a search box, only to have follow on questions prompt you for further clarification and with each one, further frustration. Oh and BTW, the same FAQ pages which e-commerce sites laughably call ‘customer support’ –

“ And, I wonder why your ASCI customer service scores are soo low Mr. or Mrs. e-Retailer :),” says this blogger facetiously, to her audience .

 

 

 

And, we’re not talking about fuzzy logic either – Simply put, fuzzy logic is fun to say, yes, and technically is:

fuzzy logic

–> Rule-based technology with exceptions (see arrow 4)

–> Represents linguistic categories (for example, “warm”, “hot”) as ranges of values

–> Describes a particular phenomenon or process and then represents in a diminutive number of flexible rules

–> Provides solutions to scenarios typically difficult to represent with succinct IF-THEN rules

(Graphic: Take a thermostat in your home and assign membership functions for the input called temperature. This becomes part of the logic of the thermostat to control the room temperature. Membership functions translate linguistic expressions such as “warm” or “cool” into quantifiable numbers that computer systems can then consume and manipulate.)

 

Nope, we are talking Neural Networks – the absolute Bees-Knees in my mind, right up there with social intelligence and my family (in no specific order :):

–> Find patterns and relationships in massive amounts of data that are too complicated for human to analyze

–> “Learn” patterns by searching for relationships, building models, and correcting over and over again model’s own mistakes

–> Humans “train” network by feeding it training data for which inputs produce known set of outputs or conclusions, to help neural network learn correct solution by example

–> Neural network applications in medicine, science, and business address problems in pattern classification, prediction, financial analysis, and control and optimization

 

Remember folks: Knowledge is power and definitely an asset. Want to know more? I discuss this and other intangibles further in part 1 of a multi-part study I am conducting called:

weemee Measuring Our Intangible Assets, by Laura Edell